Jittor is a high-performance deep learning framework based on JIT compiling and meta-operators.

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Deep Learning jittor
Overview

Jittor: a Just-in-time(JIT) deep learning framework

Jittor Logo

Quickstart | Install | Tutorial | Chinese

Jittor is a high-performance deep learning framework based on JIT compiling and meta-operators. The whole framework and meta-operators are compiled just-in-time. A powerful op compiler and tuner are integrated into Jittor. It allowed us to generate high-performance code with specialized for your model. Jittor also contains a wealth of high-performance model libraries, including: image recognition, detection, segmentation, generation, differentiable rendering, geometric learning, reinforcement learning, etc. .

The front-end language is Python. Module Design and Dynamic Graph Execution is used in the front-end, which is the most popular design for deeplearning framework interface. The back-end is implemented by high performance language, such as CUDA,C++.

Related Links:

The following example shows how to model a two-layer neural network step by step and train from scratch In a few lines of Python code.

import jittor as jt
from jittor import Module
from jittor import nn
import numpy as np

class Model(Module):
    def __init__(self):
        self.layer1 = nn.Linear(1, 10)
        self.relu = nn.Relu() 
        self.layer2 = nn.Linear(10, 1)
    def execute (self,x) :
        x = self.layer1(x)
        x = self.relu(x)
        x = self.layer2(x)
        return x

def get_data(n): # generate random data for training test.
    for i in range(n):
        x = np.random.rand(batch_size, 1)
        y = x*x
        yield jt.float32(x), jt.float32(y)


learning_rate = 0.1
batch_size = 50
n = 1000

model = Model()
optim = nn.SGD(model.parameters(), learning_rate)

for i,(x,y) in enumerate(get_data(n)):
    pred_y = model(x)
    dy = pred_y - y
    loss = dy * dy
    loss_mean = loss.mean()
    optim.step(loss_mean)
    print(f"step {i}, loss = {loss_mean.data.sum()}")

Contents

Quickstart

We provide some jupyter notebooks to help you quick start with Jittor.

Install

Jittor environment requirements:

  • System: Linux(e.g. Ubuntu/CentOS/Arch), macOS, or Windows, enviroment requirements of Linux and Mac are list below:

  • Python version >= 3.7

  • CPU compiler (require at least one of the following)

    • g++ (>=5.4.0)
    • clang (>=8.0)
  • GPU compiler (optional)

    • nvcc (>=10.0 for g++ or >=10.2 for clang)
  • GPU library: cudnn-dev (recommend tar file installation, reference link)

Windows requirements atr:

Note#1: macOS users have to install additional dependencies, see macOS install.

Jittor offers three ways to install: pip, docker, or manual.

Pip install

sudo apt install python3.7-dev libomp-dev
python3.7 -m pip install jittor
# or install from github(latest version)
# python3.7 -m pip install git+https://github.com/Jittor/jittor.git
python3.7 -m jittor.test.test_example

macOS install

Please first install additional dependencies with homebrew.

brew install [email protected] onednn libomp

Then you can install jittor through pip and run the example.

python3.7 -m pip install jittor
python3.7 -m jittor.test.test_example

Currently jittor only supports CPU in macOS.

Windows install

# check your python version(>=3.8)
python --version
python -m pip install jittor
# if conda is used
conda install pywin32

In Windows, jittor will automatically detect and install CUDA, please make sure your NVIDIA driver support CUDA 10.2 or above, or you can manually let jittor install CUDA for you:

python -m jittor_utils.install_cuda

Docker Install

We provide a Docker installation method to save you from configuring the environment. The Docker installation method is as follows:

# CPU only(Linux)
docker run -it --network host jittor/jittor
# CPU and CUDA(Linux)
docker run -it --network host --gpus all jittor/jittor-cuda
# CPU only(Mac and Windows)
docker run -it -p 8888:8888 jittor/jittor

manual install

We will show how to install Jittor in Ubuntu 16.04 step by step, Other Linux distributions may have similar commands.

Step 1: Choose your back-end compiler

# g++
sudo apt install g++ build-essential libomp-dev

# OR clang++-8
wget -O - https://raw.githubusercontent.com/Jittor/jittor/master/script/install_llvm.sh > /tmp/llvm.sh
bash /tmp/llvm.sh 8

Step 2: Install Python and python-dev

Jittor need python version >= 3.7.

sudo apt install python3.7 python3.7-dev

Step 3: Run Jittor

The whole framework is compiled Just-in-time. Let's install jittor via pip

git clone https://github.com/Jittor/jittor.git
sudo pip3.7 install ./jittor
export cc_path="clang++-8"
# if other compiler is used, change cc_path
# export cc_path="g++"
# export cc_path="icc"

# run a simple test
python3.7 -m jittor.test.test_example

if the test is passed, your Jittor is ready.

Optional Step 4: Enable CUDA

Using CUDA in Jittor is very simple, Just setup environment value nvcc_path

# replace this var with your nvcc location 
export nvcc_path="/usr/local/cuda/bin/nvcc" 
# run a simple cuda test
python3.7 -m jittor.test.test_cuda 

if the test is passed, your can use Jittor with CUDA by setting use_cuda flag.

import jittor as jt
jt.flags.use_cuda = 1

Optional Step 5: Test Resnet18 training

To check the integrity of Jittor, you can run Resnet18 training test. Note: 6G GPU RAM is requires in this test.

python3.7 -m jittor.test.test_resnet

if those tests are failed, please report bugs for us, and feel free to contribute ^_^

Tutorial

In the tutorial section, we will briefly explain the basic concept of Jittor.

To train your model with Jittor, there are only three main concepts you need to know:

  • Var: basic data type of jittor
  • Operations: Jittor'op is simular with numpy

Var

First, let's get started with Var. Var is the basic data type of jittor. Computation process in Jittor is asynchronous for optimization. If you want to access the data, Var.data can be used for synchronous data accessing.

import jittor as jt
a = jt.float32([1,2,3])
print (a)
print (a.data)
# Output: float32[3,]
# Output: [ 1. 2. 3.]

And we can give the variable a name.

a.name('a')
print(a.name())
# Output: a

Operations

Jittor'op is simular with numpy. Let's try some operations. We create Var a and b via operation jt.float32, and add them. Printing those variables shows they have the same shape and dtype.

import jittor as jt
a = jt.float32([1,2,3])
b = jt.float32([4,5,6])
c = a*b
print(a,b,c)
print(type(a), type(b), type(c))
# Output: float32[3,] float32[3,] float32[3,]
# Output: <class 'jittor_core.Var'> <class 'jittor_core.Var'> <class 'jittor_core.Var'>

Beside that, All the operators we used jt.xxx(Var, ...) have alias Var.xxx(...). For example:

c.max() # alias of jt.max(c)
c.add(a) # alias of jt.add(c, a)
c.min(keepdims=True) # alias of jt.min(c, keepdims=True)

if you want to know all the operation which Jittor supports. try help(jt.ops). All the operation you found in jt.ops.xxx, can be used via alias jt.xxx.

help(jt.ops)
# Output:
#   abs(x: core.Var) -> core.Var
#   add(x: core.Var, y: core.Var) -> core.Var
#   array(data: array) -> core.Var
#   binary(x: core.Var, y: core.Var, op: str) -> core.Var
#   ......

More

If you want to know more about Jittor, please check out the notebooks below:

Those notebooks can be started in your own computer by python3.7 -m jittor.notebook

Contributing

Jittor is still young. It may contain bugs and issues. Please report them in our bug track system. Contributions are welcome. Besides, if you have any ideas about Jittor, please let us know.

You can help Jittor in the following ways:

  • Citing Jittor in your paper
  • recommend Jittor to your friends
  • Contributing code
  • Contributed tutorials and documentation
  • File an issue
  • Answer jittor related questions
  • Light up the stars
  • Keep an eye on jittor
  • ......

Contact Us

Website: http://cg.cs.tsinghua.edu.cn/jittor/

Email: [email protected]

File an issue: https://github.com/Jittor/jittor/issues

QQ Group: 761222083

The Team

Jittor is currently maintained by the Tsinghua CSCG Group. If you are also interested in Jittor and want to improve it, Please join us!

Citation

@article{hu2020jittor,
  title={Jittor: a novel deep learning framework with meta-operators and unified graph execution},
  author={Hu, Shi-Min and Liang, Dun and Yang, Guo-Ye and Yang, Guo-Wei and Zhou, Wen-Yang},
  journal={Science China Information Sciences},
  volume={63},
  number={222103},
  pages={1--21},
  year={2020}
}

License

Jittor is Apache 2.0 licensed, as found in the LICENSE.txt file.

Issues
  • 在 ArchLinux 系统中 Jittor CUDA 无法使用

    在 ArchLinux 系统中 Jittor CUDA 无法使用

    按照 pip 方式安装后任何方式皆无法运行,下为执行日志,系统为 Arch Linux,Linux 系统内核版本为 Linux 5.13 rc4

    >>> import jittor
    [i 0604 13:52:10.823614 92 compiler.py:857] Jittor(1.2.3.14) src: /home/peter/.local/lib/python3.8/site-packages/jittor
    [i 0604 13:52:10.828678 92 compiler.py:858] g++ at /usr/bin/g++(11.1.0)
    [i 0604 13:52:10.828826 92 compiler.py:859] cache_path: /home/peter/.cache/jittor/default/g++
    [i 0604 13:52:10.839448 92 __init__.py:258] Found nvcc(11.3.58) at /opt/cuda/bin/nvcc.
    [i 0604 13:52:10.963726 92 __init__.py:258] Found gdb(10.2) at /usr/bin/gdb.
    [i 0604 13:52:10.970791 92 __init__.py:258] Found addr2line(2.36.1) at /usr/bin/addr2line.
    [i 0604 13:52:10.999637 92 compiler.py:915] py_include: -I/opt/anaconda/include/python3.8 -I/opt/anaconda/include/python3.8
    [i 0604 13:52:11.027781 92 compiler.py:917] extension_suffix: .cpython-38-x86_64-linux-gnu.so
    Traceback (most recent call last):
      File "<stdin>", line 1, in <module>
      File "/home/peter/.local/lib/python3.8/site-packages/jittor/__init__.py", line 18, in <module>
        from . import compiler
      File "/home/peter/.local/lib/python3.8/site-packages/jittor/compiler.py", line 929, in <module>
        check_cache_compile()
      File "/home/peter/.local/lib/python3.8/site-packages/jittor/compiler.py", line 800, in check_cache_compile
        assert jit_utils.cc
    AssertionError
    
    opened by MrPeterJin 28
  • can not install llvm

    can not install llvm

    I followed the install instruction to install llvm on Ubuntu 16.04 but failed with

    Ign:27 http://ppa.launchpad.net/mc3man/trusty-media/ubuntu xenial/main i386 Packages
    Ign:28 http://ppa.launchpad.net/mc3man/trusty-media/ubuntu xenial/main all Packages
    Ign:29 http://ppa.launchpad.net/mc3man/trusty-media/ubuntu xenial/main Translation-en_US
    Ign:30 http://ppa.launchpad.net/mc3man/trusty-media/ubuntu xenial/main Translation-en
    Ign:31 http://ppa.launchpad.net/mc3man/trusty-media/ubuntu xenial/main amd64 DEP-11 Metadata
    Ign:32 http://ppa.launchpad.net/mc3man/trusty-media/ubuntu xenial/main DEP-11 64x64 Icons
    Ign:33 http://ppa.launchpad.net/jonathonf/python-3.6/ubuntu xenial/main amd64 Packages
    Ign:34 http://ppa.launchpad.net/jonathonf/python-3.6/ubuntu xenial/main i386 Packages
    Ign:21 http://ppa.launchpad.net/jonathonf/python-3.6/ubuntu xenial/main all Packages
    Ign:22 http://ppa.launchpad.net/jonathonf/python-3.6/ubuntu xenial/main Translation-en_US
    Ign:35 http://ppa.launchpad.net/jonathonf/python-3.6/ubuntu xenial/main Translation-en
    Ign:24 http://ppa.launchpad.net/jonathonf/python-3.6/ubuntu xenial/main amd64 DEP-11 Metadata
    Ign:25 http://ppa.launchpad.net/jonathonf/python-3.6/ubuntu xenial/main DEP-11 64x64 Icons
    Err:26 http://ppa.launchpad.net/mc3man/trusty-media/ubuntu xenial/main amd64 Packages
      404  Not Found [IP: 2001:67c:1560:8008::15 80]
    Ign:27 http://ppa.launchpad.net/mc3man/trusty-media/ubuntu xenial/main i386 Packages
    Ign:28 http://ppa.launchpad.net/mc3man/trusty-media/ubuntu xenial/main all Packages
    Ign:29 http://ppa.launchpad.net/mc3man/trusty-media/ubuntu xenial/main Translation-en_US
    Ign:30 http://ppa.launchpad.net/mc3man/trusty-media/ubuntu xenial/main Translation-en
    Ign:31 http://ppa.launchpad.net/mc3man/trusty-media/ubuntu xenial/main amd64 DEP-11 Metadata
    Ign:32 http://ppa.launchpad.net/mc3man/trusty-media/ubuntu xenial/main DEP-11 64x64 Icons
    Err:33 http://ppa.launchpad.net/jonathonf/python-3.6/ubuntu xenial/main amd64 Packages
      403  Forbidden [IP: 2001:67c:1560:8008::15 80]
    Ign:34 http://ppa.launchpad.net/jonathonf/python-3.6/ubuntu xenial/main i386 Packages
    Ign:35 http://ppa.launchpad.net/jonathonf/python-3.6/ubuntu xenial/main Translation-en
    Reading package lists... Done
    W: The repository 'http://ppa.launchpad.net/jonathonf/python-3.6/ubuntu xenial Release' does not have a Release file.
    N: Data from such a repository can't be authenticated and is therefore potentially dangerous to use.
    N: See apt-secure(8) manpage for repository creation and user configuration details.
    W: The repository 'http://ppa.launchpad.net/mc3man/trusty-media/ubuntu xenial Release' does not have a Release file.
    N: Data from such a repository can't be authenticated and is therefore potentially dangerous to use.
    N: See apt-secure(8) manpage for repository creation and user configuration details.
    E: Failed to fetch http://ppa.launchpad.net/mc3man/trusty-media/ubuntu/dists/xenial/main/binary-amd64/Packages  404  Not Found [IP: 2001:67c:1560:8008::15 80]
    E: Failed to fetch http://ppa.launchpad.net/jonathonf/python-3.6/ubuntu/dists/xenial/main/binary-amd64/Packages  403  Forbidden [IP: 2001:67c:1560:8008::15 80]
    E: Some index files failed to download. They have been ignored, or old ones used instead.
    

    How to solve this problem?

    opened by hiyyg 11
  • 无网络环境安装jittor:undefined symbol: _ZN6jittor4Node11tflag_count

    无网络环境安装jittor:undefined symbol: _ZN6jittor4Node11tflag_count

    执行顺序 git clone https://github.com/Jittor/jittor.git sudo pip install ./jittor

    python -m jittor.test.test_example

    错误 urllib.error.URLError: <urlopen error [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: unable to get local issuer certificate (_ssl.c:1108)>

    注释掉以下代码后 version_file = os.path.join(jittor_path, "version") if os.path.isfile(version_file) and not os.path.isdir(os.path.join(jittor_path, "src", "data")): with open(version_file, 'r') as f: version = f.read().strip() # key = f"{version}-{cc_type}-{'cuda' if has_cuda else 'cpu'}.o" key = f"{version}-g++-cpu" os_id = os_release["ID"] os_key = os_type.get(os_id, "ubuntu") if "os_key" in os.environ: os_key = os.environ['os_key'] if platform.machine()=='aarch64': os_key += '-aarch64' LOG.i("OS type:", os_id, " OS key:", os_key) key += '-' + os_key + '.o' # TODO: open the website extra_obj = os.path.join(cache_path, key) url = os.path.join("https://cg.cs.tsinghua.edu.cn/jittor/assets/build/"+key) jit_utils.download(url, extra_obj) files.append(extra_obj)

    另一个错误 ImportError: .cache/jittor/default/g++/jittor_core.cpython-38-x86_64-linux-gnu.so: undefined symbol: _ZN6jittor4Node11tflag_countE

    opened by FullZing 10
  • 模型训练和预测时间不正常

    模型训练和预测时间不正常

    1.模型(ResNet)代码:

    import jittor as jt
    from jittor import nn
    from jittor import Module
    from jittor import init
    from jittor.contrib import concat, argmax_pool
    import time
    
    __all__ = ["ResNet", "ResNet18", "ResNet34", "ResNet50", "ResNet101", "ResNet152"]
    
    
    class BasicBlock(Module):
        expansion = 1
    
        def __init__(self, inplanes, planes, stride=1, downsample=None):
            self.conv1 = nn.Conv(inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
            self.bn1 = nn.BatchNorm(planes)
            self.relu = nn.Relu()
            self.conv2 = nn.Conv(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
            self.bn2 = nn.BatchNorm(planes)
            self.downsample = downsample
            self.stride = stride
            self.planes = planes
    
        def execute(self, x):
            residual = x
            out = self.conv1(x)
            out = self.bn1(out)
            out = self.relu(out)
            out = self.conv2(out)
            out = self.bn2(out)
    
            if self.downsample is not None:
                residual = self.downsample(x)
    
            out += residual
            out = self.relu(out)
            return out
    
    
    class Bottleneck(Module):
        expansion = 4
    
        def __init__(self, inplanes, planes, stride=1, downsample=None):
            self.conv1 = nn.Conv(inplanes, planes, kernel_size=1, bias=False)
            self.bn1 = nn.BatchNorm(planes)
            self.conv2 = nn.Conv(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
            self.bn2 = nn.BatchNorm(planes)
            self.conv3 = nn.Conv(planes, planes * self.expansion, kernel_size=1, bias=False)
            self.bn3 = nn.BatchNorm(planes * self.expansion)
            self.relu = nn.Relu()
            self.downsample = downsample
            self.stride = stride
    
        def execute(self, x):
            residual = x
    
            out = self.conv1(x)
            out = self.bn1(out)
            out = self.relu(out)
    
            out = self.conv2(out)
            out = self.bn2(out)
            out = self.relu(out)
    
            out = self.conv3(out)
            out = self.bn3(out)
    
            if self.downsample is not None:
                residual = self.downsample(x)
    
            out += residual
            out = self.relu(out)
            return out
    
    
    class ResNet(Module):
        def __init__(self, block, layers, num_classes=1000):
            self.inplanes = 64
            self.conv1 = nn.Conv(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
            self.bn1 = nn.BatchNorm(64)
            self.relu = nn.Relu()
            self.maxpool = nn.Pool(kernel_size=3, stride=2, padding=1)
            self.layer1 = self._make_layer(block, 64, layers[0])
            self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
            self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
            self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
            self.avgpool = nn.Pool(7, stride=1, op="mean")
            self.fc = nn.Linear(512 * block.expansion, num_classes)
    
        def _make_layer(self, block, planes, blocks, stride=1):
            downsample = None
            if stride != 1 or self.inplanes != planes * block.expansion:
                downsample = nn.Sequential(
                    nn.Conv(self.inplanes, planes * block.expansion,
                            kernel_size=1, stride=stride, bias=False),
                    nn.BatchNorm(planes * block.expansion),
                )
    
            layers = []
            layers.append(block(self.inplanes, planes, stride, downsample))
            self.inplanes = planes * block.expansion
            for i in range(1, blocks):
                layers.append(block(self.inplanes, planes))
    
            return nn.Sequential(*layers)
    
        def execute(self, x):
            x = self.conv1(x)
            x = self.bn1(x)
            x = self.relu(x)
            x = self.maxpool(x)
            x = self.layer1(x)
            x = self.layer2(x)
            x = self.layer3(x)
            x = self.layer4(x)
    
            x = self.avgpool(x)
            x = jt.reshape(x, [x.shape[0], -1])
            x = self.fc(x)
    
            return x
    
    
    def ResNet18():
        model = ResNet(BasicBlock, [2, 2, 2, 2])
        return model
    
    
    def ResNet34():
        model = ResNet(BasicBlock, [3, 4, 6, 3])
        return model
    
    
    def ResNet50():
        model = ResNet(Bottleneck, [3, 4, 6, 3])
        return model
    
    
    def ResNet101():
        model = ResNet(Bottleneck, [3, 4, 23, 3])
        return model
    
    
    def ResNet152():
        model = ResNet(Bottleneck, [3, 8, 36, 3])
        return model
    

    2.模型VGG代码

    # ***************************************************************
    # Copyright (c) 2020 Jittor. Authors:
    #     Guoye Yang <[email protected]>
    #     Dun Liang <[email protected]>.
    # All Rights Reserved.
    # This file is subject to the terms and conditions defined in
    # file 'LICENSE.txt', which is part of this source code package.
    # ***************************************************************
    import jittor as jt
    from jittor import nn
    
    
    __all__ = ['VGG', 'VGG11', 'VGG11_bn', 'VGG13', 'VGG13_bn', 'VGG16', 'VGG16_bn',
        'VGG19_bn', 'VGG19']
    
    
    class VGG(nn.Module):
    
        def __init__(self, features, num_classes=1000, init_weights=True):
            super(VGG, self).__init__()
            self.features = features
            self.classifier = nn.Sequential(
                nn.Linear(512 * 7 * 7, 4096),
                nn.ReLU(),
                nn.Dropout(),
                nn.Linear(4096, 4096),
                nn.ReLU(),
                nn.Dropout(),
                nn.Linear(4096, num_classes),
            )
    
        def execute(self, x):
            x = self.features(x)
            x = jt.reshape(x, [x.shape[0],-1])
            x = self.classifier(x)
            return x
    
    def make_layers(cfg, batch_norm=False):
        layers = []
        in_channels = 3
        for v in cfg:
            if v == 'M':
                layers += [nn.Pool(kernel_size=2, stride=2, op="maximum")]
            else:
                conv2d = nn.Conv(in_channels, v, kernel_size=3, padding=1)
                if batch_norm:
                    layers += [conv2d, nn.BatchNorm(v), nn.ReLU()]
                else:
                    layers += [conv2d, nn.ReLU()]
                in_channels = v
        return nn.Sequential(*layers)
    
    
    cfgs = {
        'A': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
        'B': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
        'D': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],
        'E': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'],
    }
    
    
    def _vgg(arch, cfg, batch_norm, **kwargs):
        model = VGG(make_layers(cfgs[cfg], batch_norm=batch_norm), **kwargs)
        return model
    
    
    def VGG11(**kwargs):
        r"""VGG 11-layer model (configuration "A") from
        `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`_
        """
        return _vgg('vgg11', 'A', False, **kwargs)
    
    
    def VGG11_bn(**kwargs):
        r"""VGG 11-layer model (configuration "A") with batch normalization
        `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`_
        """
        return _vgg('vgg11_bn', 'A', True, **kwargs)
    
    
    def VGG13(**kwargs):
        r"""VGG 13-layer model (configuration "B")
        `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`_
        """
        return _vgg('vgg13', 'B', False, **kwargs)
    
    
    def VGG13_bn(**kwargs):
        r"""VGG 13-layer model (configuration "B") with batch normalization
        `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`_
        """
        return _vgg('vgg13_bn', 'B', True, **kwargs)
    
    
    def VGG16(**kwargs):
        r"""VGG 16-layer model (configuration "D")
        `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`_
        """
        return _vgg('vgg16', 'D', False, **kwargs)
    
    
    def VGG16_bn(**kwargs):
        r"""VGG 16-layer model (configuration "D") with batch normalization
        `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`_
        """
        return _vgg('vgg16_bn', 'D', True, **kwargs)
    
    
    def VGG19(**kwargs):
        r"""VGG 19-layer model (configuration "E")
        `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`_
        """
        return _vgg('vgg19', 'E', False, **kwargs)
    
    
    def VGG19_bn(**kwargs):
        r"""VGG 19-layer model (configuration 'E') with batch normalization
        `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`_
        """
        return _vgg('vgg19_bn', 'E', True, **kwargs)
    
    

    3.模型测速代码:

    import argparse
    import os
    import re
    import subprocess
    import time
    import models.resnet as res_models
    import models.vgg as vgg_models
    import numpy as np
    from jittor import nn
    import jittor as jt
    jt.flags.use_cuda = 1
    
    
    def parse_args():
        parser = argparse.ArgumentParser()
        parser.add_argument('-a', '--arch', default='ResNet18', type=str)
    
        parser.add_argument('--batch-size', type=int, default=64)  # 128
        parser.add_argument('--learning-rate', type=float, default=0.025)
        parser.add_argument('--momentum', type=float, default=0.9)
        parser.add_argument('--weight-decay', type=float, default=1e-4)
    
        parser.add_argument('--dynamic', action='store_true')
        parser.add_argument('--single', action='store_false')
        parser.add_argument('--compiled', action='store_true')
        parser.add_argument('--step', type=int, default=100)
    
        return parser.parse_args()
    
    
    def main():
        args = parse_args()
        worker(args)
    
    def worker(args):
        if 'ResNet' in args.arch:
            model = res_models.__dict__[args.arch]()
        elif 'VGG' in args.arch:
            model = vgg_models.__dict__[args.arch]()
        else:
            raise NotImplementedError
    
        def train_func():
            model.train()
    
            logits = model(image)
            loss = nn.cross_entropy_loss(logits, label, ignore_index=255)
            optimizer.step(loss)
    
        def eval_func():
            model.eval()
            logits = model(image)
    
    
        image = np.random.random([args.batch_size, 3, 224, 224]).astype(np.float32)
        label = np.random.randint(1000, size=[args.batch_size , 1])
        image = jt.float32(image)
        label = jt.int64(label)
        optimizer = nn.SGD(model.parameters(), args.learning_rate, args.momentum, args.weight_decay)
    
    
        for i in range(10):
            train_func()
    
        train_start_time = time.time()
        for i in range(args.step):
            train_func()
    
        train_time = (time.time() - train_start_time) / args.step
    
        eval_start_time = time.time()
        for i in range(args.step):
            eval_func()
    
        eval_time = (time.time() - eval_start_time) / args.step
        stdout = subprocess.getoutput('nvidia-smi')
        mem = re.findall(r'\|  (.*?)MiB /', stdout)[0].strip()
    
        print('Jittor, Model:{0},Dy:{1},#GPU:{2},batch_size:{3},mem:{4}M, avg_train_time:{5:.3f}ms, avg_eval_time:{6:.3f}ms'. \
            format(args.arch, args.dynamic, 1, args.batch_size, mem, train_time * 1000, eval_time * 1000))
    
    
    if __name__ == "__main__":
        main()
    

    模型测速

    • 测试设置:batch_size=64, GPU:1张1080ti, 测试迭代次数: 100;

      • 测试模型代码由jittor repo给出,
      • 训练和预测代码根据jittor exmaple写出。
    • 测试结果

    |Model | avg training time/iter | avg evaluate time/iter| |------- |--- | --- | ResNet-18 | 107.17ms | 1.26ms VGG-16 | 1.44ms |0.44ms

    • 从测试结果看,提出以下问题: 1.ResNet和VGG都存在inference time非常短,其他深度学习框架约几十毫秒。请问是上述测试代码中,对于jittor的使用存在问题么? 2.VGG-16的每个iter训练时间也非常短,极不正常。 期望得到作者的解答,非常感谢。
    opened by larenzhang 9
  • 多代码并行的时候sync报错

    多代码并行的时候sync报错

    同时运行两个代码的时候第二个代码会报错:

    RuntimeError: Wrong inputs arguments, Please refer to examples(help(jt.sync)).

    Types of your inputs are: self = Var, args = (),

    The function declarations are: void sync(bool device_sync = false)

    Failed reason:[f 0909 17:29:40.576724 60 executor.cc:527] Execute fused operator(2324/4776) failed: [Op(0x5623cd93d830:0:0:1:i1:o1:s0,broadcast_to->0x5623cd93d8e0),Op(0x5623cf951be0:0: 0:1:i1:o1:s0,broadcast_to->0x5623cf951c90),Op(0x5623cf9534b0:0:0:1:i2:o1:s0,binary.multiply->0x5623cf953540),Op(0x5623cfb976e0:0:0:1:i1:o1:s0,reindex_reduce.add->0x5623cf9565c0),]

    Reason: [f 0909 17:29:39.839614 60 helper_cuda.h:126] CUDA error at ~/.cache/jittor/default/g++/jit/cudnn_conv_backward_x_Tx:float32__Ty:float32__Tw:float32__XFORMAT:abcd__WF ORMAT:oihw__YFOR...hash:74f24b7a5fa4fe17_op.cc:167 code=4( CUDNN_STATUS_INTERNAL_ERROR ) cudnnFindConvolutionBackwardDataAlgorithmEx( handle_, cudnnFdesc, w->ptr(), cudnnOdesc, y->ptr(), cudnnConvDesc, cudnnIdesc, x->ptr(), num_algos, &perf_count, perf_results, ws, max_ws_size) [e 0909 17:29:40.891578 60 helper_cuda.h:115] Peek CUDA error at ~/anaconda3/envs/py3/lib/python3.8/site-packages/jittor/src/mem/allocator/cuda_dual_allocator.h:101 code=700 ( cudaErrorIllegalAddress ) _cudaLaunchHostFunc(0, &to_free_allocation, 0) terminate called after throwing an instance of 'std::runtime_error'
    what(): [f 0909 17:29:42.170272 60 helper_cuda.h:126] CUDA error at ~/anaconda3/envs/py3/lib/python3.8/site-packages/jittor/extern/cuda/cudnn/src/cudnn_warper.cc:34 code= 4( CUDNN_STATUS_INTERNAL_ERROR ) cudnnDestroy(cudnn_handle)

    请问下是什么问题?

    opened by sunhm15 6
  • 未通过Jittor测试(test_example)

    未通过Jittor测试(test_example)

    按照标准流程手动安装,输出如下:

    > python -m jittor.test.test_example
    [i 1121 15:17:16.379761 12 __init__.py:257] Found g++(10.2.0) at g++
    [i 1121 15:17:16.420423 12 compiler.py:839] Jittor(1.2.1.1) src: /usr/lib/python3.8/site-packages/jittor
    [i 1121 15:17:16.420647 12 compiler.py:840] cache_path: /home/peter/.cache/jittor/default/g++
    [i 1121 15:17:16.427812 12 compiler.py:791] Found /usr/local/cuda/bin/nvcc(11.1.105) at /opt/cuda/bin/nvcc
    [i 1121 15:17:16.522639 12 __init__.py:249] Found gdb(10.1) at /usr/bin/gdb.
    [i 1121 15:17:16.529103 12 __init__.py:249] Found addr2line(2.35.1) at /usr/bin/addr2line.
    [i 1121 15:17:16.627024 12 compiler.py:889] pybind_include: -I/usr/include/python3.8 -I/usr/lib/python3.8/site-packages/pybind11/include
    [i 1121 15:17:16.677954 12 compiler.py:891] extension_suffix: .cpython-38-x86_64-linux-gnu.so
    g++: error: /usr/lib/python3.8/site-packages/jittor/src/utils/cache_compile.cc: Not a directory
    g++: error: /usr/lib/python3.8/site-packages/jittor/src/utils/log.cc: Not a directory
    g++: error: /usr/lib/python3.8/site-packages/jittor/src/utils/tracer.cc: Not a directory
    g++: error: /usr/lib/python3.8/site-packages/jittor/src/utils/jit_utils.cc: Not a directory
    Traceback (most recent call last):
      File "/usr/lib/python3.8/runpy.py", line 185, in _run_module_as_main
        mod_name, mod_spec, code = _get_module_details(mod_name, _Error)
      File "/usr/lib/python3.8/runpy.py", line 111, in _get_module_details
        __import__(pkg_name)
      File "/usr/lib/python3.8/site-packages/jittor/__init__.py", line 16, in <module>
        from . import compiler
      File "/usr/lib/python3.8/site-packages/jittor/compiler.py", line 901, in <module>
        check_cache_compile()
      File "/usr/lib/python3.8/site-packages/jittor/compiler.py", line 775, in check_cache_compile
        recompile = compile(cc_path, cc_flags+f" {opt_flags} ", files, 'jit_utils_core'+extension_suffix, True)
      File "/usr/lib/python3.8/site-packages/jittor/compiler.py", line 69, in compile
        return do_compile(cmd)
      File "/usr/lib/python3.8/site-packages/jittor/compiler.py", line 47, in do_compile
        run_cmd(cmd)
      File "/usr/lib/python3.8/site-packages/jittor_utils/__init__.py", line 136, in run_cmd
        raise Exception(err_msg)
    Exception: Run cmd failed: g++ /usr/lib/python3.8/site-packages/jittor/src/utils/cache_compile.cc /usr/lib/python3.8/site-packages/jittor/src/utils/log.cc /usr/lib/python3.8/site-packages/jittor/src/utils/tracer.cc /usr/lib/python3.8/site-packages/jittor/src/utils/jit_utils.cc   -Wall -Werror -Wno-unknown-pragmas -std=c++14 -fPIC -march=native  -fdiagnostics-color=always -I/usr/include/python3.8 -I/usr/lib/python3.8/site-packages/pybind11/include -I/usr/lib/python3.8/site-packages/jittor/src   -O2    -lstdc++ -ldl -shared  -o /home/peter/.cache/jittor/default/g++/jit_utils_core.cpython-38-x86_64-linux-gnu.so
    

    系统基本配置:

    OS: Manjaro Linux x86_64 
    Host: 80RQ Lenovo Rescuer-15ISK
    Kernel: 5.9.8-2-MANJARO 
    
    opened by MrPeterJin 6
  • 设置seed输出还是会有不同

    设置seed输出还是会有不同

    你好,我在跑pointnet++的时候有一个问题,我设置了jt.seed()以及np.random.seed(),并且吧dataset的shuffle去掉了,但是输出仍然有所不同,这是前几个batch的loss,是否是我的设置问题还是存在bug?谢谢

    微信图片_20201206181501 微信图片_20201206181515

    opened by jwzxgy2007 6
  • 性能测试及显存占用

    性能测试及显存占用

    import os
    import sys
    import numpy as np
    import jittor as jt
    import jittor.models as jtmodels
    import json
    import time
    jt.flags.use_cuda=1
    a=np.random.rand(5, 3,512,512).astype('float32')
    inputs=jt.array(a)
    res=m(inputs)
    t=time.time()
    r=[]
    for i in range(10):
        a=np.random.rand(5, 3,512,512).astype('float32')
        inputs=jt.array(a)
        res=m(inputs)
        r.append(res.max().numpy().tolist()[0])
    print(time.time()-t)
    

    在v100卡上对比当前版本的计图和pytorch1.4, 同样的程序计图平均在960ms,pytorch在950ms左右,感觉没有起到加速效果 同时计图占用了15516M显存,gc后会减少到6736M,显存占用较大, 请问针对这种推理方式有优化使用的方式么

    opened by feihuidiqiu 6
  • Better compiler path searching with environment varible

    Better compiler path searching with environment varible

    I'm using module for package management on a cluster, the current environment is shown as following:

    Currently Loaded Modulefiles:
      1) compiler/intel/2019.5.281    4) tools/mumps_seq/5.2.1        7) tools/matlab/R2017a
      2) mpi/intel/2019.5.281         5) tools/superlu/5.2.1          8) compiler/cuda/10.1
      3) tools/tmux/1.8               6) tools/suitesparse/5.6.0      9) interpreter/python/3.7-gpu
    

    Note that python 3.7 and nvcc 10.1 are both loaded, and currently c/c++ compiler is icc, so let's start jittor program

    [i 0518 20:13:45.097043 52 compiler.py:851] Jittor(1.2.3.8) src: /home/users/$MYNAME$/.local/lib/python3.7/site-packages/jittor
    [i 0518 20:13:45.097205 52 compiler.py:852] g++ at /software/local/bin/g++
    [i 0518 20:13:45.097302 52 compiler.py:853] cache_path: /home/users/$MYNAME$/.cache/jittor/default/g++
    which: no nvcc in (/usr/local/cuda/bin)
    [i 0518 20:13:45.195274 52 __init__.py:255] Found gdb(7.6.1) at /bin/gdb.
    [i 0518 20:13:45.212988 52 __init__.py:255] Found addr2line(7.3) at /bin/addr2line.
    [i 0518 20:13:45.338448 52 compiler.py:893] py_include: -I/software/python/virtualenv/py3.7-gpu/include/python3.7m -I/software/python/virtualenv/py3.7-gpu/include/python3.7m
    [i 0518 20:13:45.437864 52 compiler.py:895] extension_suffix: .cpython-37m-x86_64-linux-gnu.so
    

    I don't know why jittor is trying to obtain g++, which i haven't loaded (so the environment is not correct for g++) and why it hasn't found nvcc, maybe compilers should be found by using current environment path

    opened by yhl1219 5
  • 关于用jittor读取torch pth文件可能的错误

    关于用jittor读取torch pth文件可能的错误

    最近使用jittor读取torch嵌套pth文件发现这样一个问题:一旦torch的sequential或者嵌套module带有名字,jittor就无法读取(和jittor sequential数字索引有关。)因此用户必须按照数字顺序命名。我自己尝试使用回溯来是的用户可以任意命名。 另外还有一个小bug:打印module时,其中的LeakyReLU层会显示MakeModule,查阅文档似乎ReLU系层都存在这个问题。

    opened by Exusial 4
  • Problem@Ubuntu 16.04@python-enviroment

    [email protected] [email protected]

    Python 3.8.8 (default, Apr 13 2021, 19:58:26) [GCC 7.3.0] :: Anaconda, Inc. on linux Type "help", "copyright", "credits" or "license" for more information.

    import jittor [i 1106 21:05:50.208856 52 compiler.py:944] Jittor(1.3.1.18) src: /home/hyper/.local/lib/python3.8/site-packages/jittor [i 1106 21:05:50.210880 52 compiler.py:945] g++ at /usr/bin/g++(5.4.0) [i 1106 21:05:50.210967 52 compiler.py:946] cache_path: /home/hyper/.cache/jittor/jt1.3.1/g++5.4.0/py3.8.8/Linux-4.15.0-1x88/IntelRCoreTMi3xc8/default [i 1106 21:05:50.213103 52 init.py:372] Found nvcc(10.1.105) at /usr/bin/nvcc. [i 1106 21:05:50.254729 52 init.py:372] Found gdb(7.11.1) at /usr/bin/gdb. [i 1106 21:05:50.256745 52 init.py:372] Found addr2line(2.26.1) at /usr/bin/addr2line. [i 1106 21:05:50.343250 52 compiler.py:997] cuda key:cu10.1.105_sm_61 Traceback (most recent call last): File "", line 1, in File "/home/hyper/.local/lib/python3.8/site-packages/jittor/init.py", line 18, in from . import compiler File "/home/hyper/.local/lib/python3.8/site-packages/jittor/compiler.py", line 1032, in py_include = jit_utils.get_py3_include_path() File "/home/hyper/.local/lib/python3.8/site-packages/jittor_utils/init.py", line 445, in get_py3_include_path _py3_include_path = run_cmd(get_py3_config_path()+" --includes") File "/home/hyper/.local/lib/python3.8/site-packages/jittor_utils/init.py", line 425, in get_py3_config_path raise RuntimeError(f"python3.{sys.version_info.minor}-config " RuntimeError: python3.8-config not found in ['/usr/bin/python3.8-config', '/usr/bin/python38-config', '/usr/bin/python3.8-config', '/usr/local/bin/python3.8-config', '/opt/homebrew/bin/python3.8-config', '/usr/bin/python3-config'], please specify enviroment variable 'python_config_path', or install python3.8-dev


    conda版本的python3.8.8, solved by copy bin/python3.8-config to /usr/bin

    opened by HyperGroups 2
  • problem@python -m jittor.test.test_example@ubuntu16+python3.8

    [email protected] -m [email protected]+python3.8

    [email protected]:/usr/local/cuda$ python -m jittor.test.test.example [i 1106 03:34:54.091816 28 compiler.py:944] Jittor(1.3.1.18) src: /home/hyper/.local/lib/python3.8/site-packages/jittor [i 1106 03:34:54.093861 28 compiler.py:945] g++ at /usr/bin/g++(5.4.0) [i 1106 03:34:54.093924 28 compiler.py:946] cache_path: /home/hyper/.cache/jittor/jt1.3.1/g++5.4.0/py3.8.8/Linux-4.15.0-1x28/IntelRCoreTMi3xc8/default [i 1106 03:34:54.101029 28 init.py:372] Found nvcc(10.1.105) at /usr/bin/nvcc. [i 1106 03:34:54.144537 28 init.py:372] Found gdb(7.11.1) at /usr/bin/gdb. [i 1106 03:34:54.148659 28 init.py:372] Found addr2line(2.26.1) at /usr/bin/addr2line. [i 1106 03:34:54.268504 28 compiler.py:997] cuda key:cu10.1.105_sm_61 [i 1106 03:34:54.304868 28 compiler.py:34] Create cache dir: /home/hyper/.cache/jittor/jt1.3.1/g++5.4.0/py3.8.8/Linux-4.15.0-1x28/IntelRCoreTMi3xc8/default/cu10.1.105_sm_61 [i 1106 03:34:54.304965 28 compiler.py:34] Create cache dir: /home/hyper/.cache/jittor/jt1.3.1/g++5.4.0/py3.8.8/Linux-4.15.0-1x28/IntelRCoreTMi3xc8/default/cu10.1.105_sm_61/jit [i 1106 03:34:54.305024 28 compiler.py:34] Create cache dir: /home/hyper/.cache/jittor/jt1.3.1/g++5.4.0/py3.8.8/Linux-4.15.0-1x28/IntelRCoreTMi3xc8/default/cu10.1.105_sm_61/obj_files [i 1106 03:34:54.305081 28 compiler.py:34] Create cache dir: /home/hyper/.cache/jittor/jt1.3.1/g++5.4.0/py3.8.8/Linux-4.15.0-1x28/IntelRCoreTMi3xc8/default/cu10.1.105_sm_61/gen [i 1106 03:34:54.305132 28 compiler.py:34] Create cache dir: /home/hyper/.cache/jittor/jt1.3.1/g++5.4.0/py3.8.8/Linux-4.15.0-1x28/IntelRCoreTMi3xc8/default/cu10.1.105_sm_61/tmp [i 1106 03:34:54.305188 28 compiler.py:34] Create cache dir: /home/hyper/.cache/jittor/jt1.3.1/g++5.4.0/py3.8.8/Linux-4.15.0-1x28/IntelRCoreTMi3xc8/default/cu10.1.105_sm_61/checkpoints [e 1106 03:34:58.600524 28 compiler.py:875] jit_utils updated, please rerun your command. [email protected]:/usr/local/cuda$ python -m jittor.test.test.example [i 1106 03:35:03.800145 32 compiler.py:944] Jittor(1.3.1.18) src: /home/hyper/.local/lib/python3.8/site-packages/jittor [i 1106 03:35:03.802495 32 compiler.py:945] g++ at /usr/bin/g++(5.4.0) [i 1106 03:35:03.802558 32 compiler.py:946] cache_path: /home/hyper/.cache/jittor/jt1.3.1/g++5.4.0/py3.8.8/Linux-4.15.0-1x28/IntelRCoreTMi3xc8/default [i 1106 03:35:03.805144 32 init.py:372] Found nvcc(10.1.105) at /usr/bin/nvcc. [i 1106 03:35:03.853370 32 init.py:372] Found gdb(7.11.1) at /usr/bin/gdb. [i 1106 03:35:03.855939 32 init.py:372] Found addr2line(2.26.1) at /usr/bin/addr2line. [i 1106 03:35:03.977698 32 compiler.py:997] cuda key:cu10.1.105_sm_61 [i 1106 03:35:09.511158 32 init.py:187] Total mem: 15.61GB, using 5 procs for compiling. nvcc fatal : Path to libdevice library not specified6ss multiprocessing.pool.RemoteTraceback: """ Traceback (most recent call last): File "/opt/anaconda3/lib/python3.8/multiprocessing/pool.py", line 125, in worker result = (True, func(*args, **kwds)) File "/home/hyper/.local/lib/python3.8/site-packages/jittor_utils/init.py", line 157, in do_compile return cc.cache_compile(cmd, cache_path, jittor_path) RuntimeError: [f 1106 03:36:01.583748 32 log.cc:569] Check failed ret(256) == 0(0) Run cmd failed: "/usr/bin/nvcc" "/home/hyper/.local/lib/python3.8/site-packages/jittor/src/misc/nan_checker.cu" -std=c++14 -Xcompiler -fPIC -Xcompiler -march=native -Xcompiler -fdiagnostics-color=always -I"/home/hyper/.local/lib/python3.8/site-packages/jittor/src" -I/opt/anaconda3/include/python3.8 -I/opt/anaconda3/include/python3.8 -DHAS_CUDA -I"/usr/local/cuda-10.1/include" -I"/home/hyper/.local/lib/python3.8/site-packages/jittor/extern/cuda/inc" -I"/home/hyper/.cache/jittor/jt1.3.1/g++5.4.0/py3.8.8/Linux-4.15.0-1x28/IntelRCoreTMi3xc8/default/cu10.1.105_sm_61" -O2 -c -o "/home/hyper/.cache/jittor/jt1.3.1/g++5.4.0/py3.8.8/Linux-4.15.0-1x28/IntelRCoreTMi3xc8/default/cu10.1.105_sm_61/obj_files/nan_checker.cu.o" -x cu --cudart=shared -ccbin="/usr/bin/g++" -w -I"/home/hyper/.local/lib/python3.8/site-packages/jittor/extern/cuda/inc" """

    The above exception was the direct cause of the following exception:

    Traceback (most recent call last): File "/opt/anaconda3/lib/python3.8/runpy.py", line 185, in _run_module_as_main mod_name, mod_spec, code = _get_module_details(mod_name, _Error) File "/opt/anaconda3/lib/python3.8/runpy.py", line 111, in get_module_details import(pkg_name) File "/home/hyper/.local/lib/python3.8/site-packages/jittor/init.py", line 18, in from . import compiler File "/home/hyper/.local/lib/python3.8/site-packages/jittor/compiler.py", line 1303, in compile(cc_path, cc_flags+opt_flags, files, 'jittor_core'+extension_suffix) File "/home/hyper/.local/lib/python3.8/site-packages/jittor/compiler.py", line 147, in compile jit_utils.run_cmds(cmds, cache_path, jittor_path, "Compiling "+base_output) File "/home/hyper/.local/lib/python3.8/site-packages/jittor_utils/init.py", line 211, in run_cmds for i, in enumerate(p.imap_unordered(do_compile, cmds)): File "/opt/anaconda3/lib/python3.8/multiprocessing/pool.py", line 868, in next raise value RuntimeError: [f 1106 03:36:01.583748 32 log.cc:569] Check failed ret(256) == 0(0) Run cmd failed: "/usr/bin/nvcc" "/home/hyper/.local/lib/python3.8/site-packages/jittor/src/misc/nan_checker.cu" -std=c++14 -Xcompiler -fPIC -Xcompiler -march=native -Xcompiler -fdiagnostics-color=always -I"/home/hyper/.local/lib/python3.8/site-packages/jittor/src" -I/opt/anaconda3/include/python3.8 -I/opt/anaconda3/include/python3.8 -DHAS_CUDA -I"/usr/local/cuda-10.1/include" -I"/home/hyper/.local/lib/python3.8/site-packages/jittor/extern/cuda/inc" -I"/home/hyper/.cache/jittor/jt1.3.1/g++5.4.0/py3.8.8/Linux-4.15.0-1x28/IntelRCoreTMi3xc8/default/cu10.1.105_sm_61" -O2 -c -o "/home/hyper/.cache/jittor/jt1.3.1/g++5.4.0/py3.8.8/Linux-4.15.0-1x28/IntelRCoreTMi3xc8/default/cu10.1.105_sm_61/obj_files/nan_checker.cu.o" -x cu --cudart=shared -ccbin="/usr/bin/g++" -w -I"/home/hyper/.local/lib/python3.8/site-packages/jittor/extern/cuda/inc"

    opened by HyperGroups 1
  • win 10,anaconda版,import jittor报错

    win 10,anaconda版,import jittor报错

    PS C:\WINDOWS\system32> python Python 3.8.8 (default, Apr 13 2021, 15:08:03) [MSC v.1916 64 bit (AMD64)] :: Anaconda, Inc. on win32

    Warning: This Python interpreter is in a conda environment, but the environment has not been activated. Libraries may fail to load. To activate this environment please see https://conda.io/activation

    Type "help", "copyright", "credits" or "license" for more information.

    import jittor [i 1103 23:45:34.640000 08 compiler.py:941] Jittor(1.3.1.16) src: D:\ProgramFiles\Anaconda3\lib\site-packages\jittor [i 1103 23:45:34.664000 08 compiler.py:942] cl at C:\Users\HyperGroups.cache\jittor\msvc\VC_____\bin\cl.exe(19.29.30133) [i 1103 23:45:34.664000 08 compiler.py:943] cache_path: C:\Users\HyperGroups.cache\jittor\jt1.3.1\cl\py3.8.8\Windows-10-10.x6e\IntelRCoreTMi7x9d\default [i 1103 23:45:34.667000 08 install_cuda.py:51] cuda_driver_version: [11, 4, 0] [i 1103 23:45:34.689000 08 init.py:372] Found C:\Users\HyperGroups.cache\jittor\jtcuda\cuda11.4_cudnn8_win\bin\nvcc.exe(11.4.100) at C:\Users\HyperGroups.cache\jittor\jtcuda\cuda11.4_cudnn8_win\bin\nvcc.exe. [i 1103 23:45:34.844000 08 init.py:372] Found gdb(8.1) at D:\ProgramFiles\mingw64\bin\gdb.EXE. [i 1103 23:45:34.902000 08 init.py:372] Found addr2line(2.30) at D:\ProgramFiles\mingw64\bin\addr2line.EXE. [i 1103 23:45:35.166000 08 compiler.py:993] cuda key:cu11.4.100_sm_86 [i 1103 23:45:35.168000 08 init.py:187] Total mem: 47.93GB, using 15 procs for compiling. [e 1103 23:45:37.213000 08 log.cc:526] cpu_math.cc D:\ProgramFiles\Anaconda3\lib\site-packages\jittor\src\misc\cpu_math.cc(47): error C2065: 'M_PI': undeclared identifier D:\ProgramFiles\Anaconda3\lib\site-packages\jittor\src\misc\cpu_math.cc(53): note: see reference to function template instantiation 'float jittor::calc_erfinv(T)' being compiled with [ T=float ] D:\ProgramFiles\Anaconda3\lib\site-packages\jittor\src\misc\cpu_math.cc(48): error C2065: 'M_PI': undeclared identifier

    multiprocessing.pool.RemoteTraceback: """ Traceback (most recent call last): File "d:\programfiles\anaconda3\lib\multiprocessing\pool.py", line 125, in worker result = (True, func(*args, **kwds)) File "D:\ProgramFiles\Anaconda3\lib\site-packages\jittor_utils_init_.py", line 157, in do_compile return cc.cache_compile(cmd, cache_path, jittor_path) RuntimeError: [f 1103 23:45:37.213000 08 log.cc:569] Check failed ret(2) == 0(0) Run cmd failed: "C:\Users\HyperGroups.cache\jittor\msvc\VC_____\bin\cl.exe" "D:\ProgramFiles\Anaconda3\lib\site-packages\jittor\src\misc\cpu_math.cc" -std:c++17 -EHa -MD -nologo -I"C:\Users\HyperGroups.cache\jittor\msvc\VC\include" -I"C:\Users\HyperGroups.cache\jittor\msvc\win10_kits\include\ucrt" -I"C:\Users\HyperGroups.cache\jittor\msvc\win10_kits\include\shared" -I"C:\Users\HyperGroups.cache\jittor\msvc\win10_kits\include\um" -DNOMINMAX -I"D:\ProgramFiles\Anaconda3\lib\site-packages\jittor\src" -I"d:\programfiles\anaconda3\include" -DHAS_CUDA -I"C:\Users\HyperGroups.cache\jittor\jtcuda\cuda11.4_cudnn8_win\include" -I"D:\ProgramFiles\Anaconda3\lib\site-packages\jittor\extern\cuda\inc" -I"C:\Users\HyperGroups.cache\jittor\jt1.3.1\cl\py3.8.8\Windows-10-10.x6e\IntelRCoreTMi7x9d\default\cu11.4.100_sm_86" -O2 -c -Fo: "C:\Users\HyperGroups.cache\jittor\jt1.3.1\cl\py3.8.8\Windows-10-10.x6e\IntelRCoreTMi7x9d\default\cu11.4.100_sm_86\obj_files\cpu_math.cc.obj" """

    The above exception was the direct cause of the following exception:

    Traceback (most recent call last): File "", line 1, in File "D:\ProgramFiles\Anaconda3\lib\site-packages\jittor_init_.py", line 18, in from . import compiler File "D:\ProgramFiles\Anaconda3\lib\site-packages\jittor\compiler.py", line 1299, in compile(cc_path, cc_flags+opt_flags, files, 'jittor_core'+extension_suffix) File "D:\ProgramFiles\Anaconda3\lib\site-packages\jittor\compiler.py", line 147, in compile jit_utils.run_cmds(cmds, cache_path, jittor_path, "Compiling "+base_output) File "D:\ProgramFiles\Anaconda3\lib\site-packages\jittor_utils_init_.py", line 211, in run_cmds for i,_ in enumerate(p.imap_unordered(do_compile, cmds)): File "D:\ProgramFiles\Anaconda3\lib\multiprocessing\pool.py", line 868, in next raise value RuntimeError: [f 1103 23:45:37.213000 08 log.cc:569] Check failed ret(2) == 0(0) Run cmd failed: "C:\Users\HyperGroups.cache\jittor\msvc\VC_____\bin\cl.exe" "D:\ProgramFiles\Anaconda3\lib\site-packages\jittor\src\misc\cpu_math.cc" -std:c++17 -EHa -MD -nologo -I"C:\Users\HyperGroups.cache\jittor\msvc\VC\include" -I"C:\Users\HyperGroups.cache\jittor\msvc\win10_kits\include\ucrt" -I"C:\Users\HyperGroups.cache\jittor\msvc\win10_kits\include\shared" -I"C:\Users\HyperGroups.cache\jittor\msvc\win10_kits\include\um" -DNOMINMAX -I"D:\ProgramFiles\Anaconda3\lib\site-packages\jittor\src" -I"d:\programfiles\anaconda3\include" -DHAS_CUDA -I"C:\Users\HyperGroups.cache\jittor\jtcuda\cuda11.4_cudnn8_win\include" -I"D:\ProgramFiles\Anaconda3\lib\site-packages\jittor\extern\cuda\inc" -I"C:\Users\HyperGroups.cache\jittor\jt1.3.1\cl\py3.8.8\Windows-10-10.x6e\IntelRCoreTMi7x9d\default\cu11.4.100_sm_86" -O2 -c -Fo: "C:\Users\HyperGroups.cache\jittor\jt1.3.1\cl\py3.8.8\Windows-10-10.x6e\IntelRCoreTMi7x9d\default\cu11.4.100_sm_86\obj_files\cpu_math.cc.obj"

    opened by HyperGroups 4
  •  python -m jittor.test.test_cudnn_op failed

    python -m jittor.test.test_cudnn_op failed

    Hi, thanks for your great work!

    When I try to install Jittor with CUDA enabled, the following problem was raised. I'd appreciate it if you can give me some ideas to fix that.

    Terminal Log

    [i 1103 17:01:10.549464 16 compiler.py:941] Jittor(1.3.1.16) src: /home/liuzhian/anaconda3/envs/jittor/lib/python3.7/site-packages/jittor
    [i 1103 17:01:10.551980 16 compiler.py:942] g++ at /usr/bin/g++(5.4.0)
    [i 1103 17:01:10.552081 16 compiler.py:943] cache_path: /home/liuzhian/.cache/jittor/jt1.3.1/g++5.4.0/py3.7.11/Linux-4.15.0-1xca/AMDRyzen536006x1a/default
    [i 1103 17:01:10.554688 16 __init__.py:372] Found nvcc(10.0.130) at /usr/local/cuda/bin/nvcc.
    [i 1103 17:01:10.592039 16 __init__.py:372] Found gdb(7.11.1) at /usr/bin/gdb.
    [i 1103 17:01:10.594744 16 __init__.py:372] Found addr2line(2.26.1) at /usr/bin/addr2line.
    [i 1103 17:01:10.681617 16 compiler.py:993] cuda key:cu10.0.130_sm_75
    [i 1103 17:01:10.836206 16 __init__.py:187] Total mem: 47.17GB, using 15 procs for compiling.
    [i 1103 17:01:10.892981 16 jit_compiler.cc:27] Load cc_path: /usr/bin/g++
    [i 1103 17:01:10.990998 16 init.cc:61] Found cuda archs: [75,]
    [i 1103 17:01:11.056757 16 __init__.py:372] Found mpicc(1.10.2) at /usr/bin/mpicc.
    [i 1103 17:01:11.134825 16 compile_extern.py:29] found /usr/local/cuda/include/cublas.h
    [i 1103 17:01:11.138702 16 compile_extern.py:29] found /usr/local/cuda/lib64/libcublas.so
    [i 1103 17:01:11.554852 16 compile_extern.py:29] found /usr/local/cuda/include/cudnn.h
    [i 1103 17:01:11.563999 16 compile_extern.py:29] found /usr/local/cuda/lib64/libcudnn.so
    [i 1103 17:01:12.492202 16 compile_extern.py:29] found /usr/local/cuda/include/curand.h
    [i 1103 17:01:12.508559 16 compile_extern.py:29] found /usr/local/cuda/lib64/libcurand.so
    [i 1103 17:01:12.566813 16 cuda_flags.cc:32] CUDA enabled.
    [i 1103 17:01:12.586208 16 v10 op.cc:258] Jit op key not found: curand_random[T:float32][R:uniform][JIT:1][JIT_cuda:1][index_t:int32]
    [i 1103 17:01:12.587537 16 v10 op.cc:265] Get jit op entry: 0x7f4a0a1e50be
    [i 1103 17:01:12.591935 16 v100 op.cc:254] Jit op key found: curand_random[T:float32][R:uniform][JIT:1][JIT_cuda:1][index_t:int32] jit op entry: 0x7f4a0a1e50be
    [i 1103 17:01:12.606935 16 v10 op.cc:258] Jit op key not found: cudnn_conv[Tx:float32][Ty:float32][Tw:float32][XFORMAT:acdb][WFORMAT:oihw][YFORMAT:acdb][JIT:1][JIT_cuda:1][index_t:int32]
    [i 1103 17:01:12.609154 16 v10 op.cc:265] Get jit op entry: 0x7f4a07beef10
    [i 1103 17:01:12.705230 16 cuda_flags.cc:32] CUDA enabled.
    [i 1103 17:01:12.705329 16 v100 op.cc:254] Jit op key found: curand_random[T:float32][R:uniform][JIT:1][JIT_cuda:1][index_t:int32] jit op entry: 0x7f4a0a1e50be
    [i 1103 17:01:12.705372 16 v100 op.cc:254] Jit op key found: curand_random[T:float32][R:uniform][JIT:1][JIT_cuda:1][index_t:int32] jit op entry: 0x7f4a0a1e50be
    [i 1103 17:01:12.719387 16 v100 op.cc:254] Jit op key found: cudnn_conv[Tx:float32][Ty:float32][Tw:float32][XFORMAT:acdb][WFORMAT:oihw][YFORMAT:acdb][JIT:1][JIT_cuda:1][index_t:int32] jit op entry: 0x7f4a07beef10
    [i 1103 17:01:12.791396 16 cuda_flags.cc:32] CUDA enabled.
    [i 1103 17:01:12.791492 16 v100 op.cc:254] Jit op key found: curand_random[T:float32][R:uniform][JIT:1][JIT_cuda:1][index_t:int32] jit op entry: 0x7f4a0a1e50be
    [i 1103 17:01:12.791535 16 v100 op.cc:254] Jit op key found: curand_random[T:float32][R:uniform][JIT:1][JIT_cuda:1][index_t:int32] jit op entry: 0x7f4a0a1e50be
    [i 1103 17:01:12.809329 16 v100 op.cc:254] Jit op key found: cudnn_conv[Tx:float32][Ty:float32][Tw:float32][XFORMAT:acdb][WFORMAT:oihw][YFORMAT:acdb][JIT:1][JIT_cuda:1][index_t:int32] jit op entry: 0x7f4a07beef10
    .[i 1103 17:01:12.880788 16 cuda_flags.cc:32] CUDA enabled.
    [i 1103 17:01:12.880968 16 v100 executor.cc:250] id: 0  type: reduce  addr: Op(0x55a73712bab0:1:1:2:i1:o1:s0,reduce.add->0x55a701115d80)
    [i 1103 17:01:12.880988 16 v100 executor.cc:259] input: 4  addr: Op(0x55a7370fa1d0:2:1:2:i2:o1:s0,binary.multiply->0x55a7365c9ff0)
    [i 1103 17:01:12.880999 16 v100 executor.cc:261] 
    [i 1103 17:01:12.881006 16 v100 executor.cc:250] id: 1  type: element  addr: Op(0x55a7370ead80:2:1:2:i2:o1:s0,binary.multiply->0x55a7365ceb70)
    [i 1103 17:01:12.881016 16 v100 executor.cc:259] input: 5  addr: Op(0x55a737110490:0:1:1:i0:o1:s0,curand_random->0x55a73710e460)
    [i 1103 17:01:12.881025 16 v100 executor.cc:259] input: 0  addr: Op(0x55a73712bab0:1:1:2:i1:o1:s0,reduce.add->0x55a701115d80)
    [i 1103 17:01:12.881033 16 v100 executor.cc:261] 
    [i 1103 17:01:12.881040 16 v100 executor.cc:250] id: 2  type: reduce  addr: Op(0x55a7370f8450:1:1:2:i1:o1:s0,reindex_reduce.add->0x55a7370f8530)
    [i 1103 17:01:12.881048 16 v100 executor.cc:259] input: 6  addr: Op(0x55a7370f0950:2:1:2:i2:o1:s0,binary.multiply->0x55a7370f09e0)
    [i 1103 17:01:12.881056 16 v100 executor.cc:261] 
    [i 1103 17:01:12.881062 16 v100 executor.cc:250] id: 3  type: reduce  addr: Op(0x55a7370f86a0:1:1:2:i1:o1:s0,reduce.add->0x55a73734ef40)
    [i 1103 17:01:12.881069 16 v100 executor.cc:259] input: 7  addr: Op(0x55a737114320:2:1:2:i2:o1:s0,binary.multiply->0x55a7371143b0)
    [i 1103 17:01:12.881077 16 v100 executor.cc:261] 
    [i 1103 17:01:12.881083 16 v100 executor.cc:250] id: 4  type: element  addr: Op(0x55a7370fa1d0:2:1:2:i2:o1:s0,binary.multiply->0x55a7365c9ff0)
    [i 1103 17:01:12.881092 16 v100 executor.cc:259] input: 8  addr: Op(0x55a73732b060:1:1:2:i1:o1:s0,reindex->0x55a73734fa30)
    [i 1103 17:01:12.881100 16 v100 executor.cc:259] input: 9  addr: Op(0x55a7365d80d0:1:1:2:i1:o1:s0,broadcast_to->0x55a7365a2900)
    [i 1103 17:01:12.881110 16 v100 executor.cc:261] 
    [i 1103 17:01:12.881116 16 v100 executor.cc:250] id: 5  type: others  addr: Op(0x55a737110490:0:1:1:i0:o1:s0,curand_random->0x55a73710e460)
    [i 1103 17:01:12.881125 16 v100 executor.cc:261] 
    [i 1103 17:01:12.881131 16 v100 executor.cc:250] id: 6  type: element  addr: Op(0x55a7370f0950:2:1:2:i2:o1:s0,binary.multiply->0x55a7370f09e0)
    [i 1103 17:01:12.881139 16 v100 executor.cc:259] input: 10  addr: Op(0x55a7370f0730:1:1:2:i1:o1:s0,broadcast_to->0x55a7370f07e0)
    [i 1103 17:01:12.881147 16 v100 executor.cc:259] input: 11  addr: Op(0x55a7370eaa40:1:1:2:i1:o1:s0,broadcast_to->0x55a73710f2f0)
    [i 1103 17:01:12.881154 16 v100 executor.cc:261] 
    [i 1103 17:01:12.881161 16 v100 executor.cc:250] id: 7  type: element  addr: Op(0x55a737114320:2:1:2:i2:o1:s0,binary.multiply->0x55a7371143b0)
    [i 1103 17:01:12.881170 16 v100 executor.cc:259] input: 12  addr: Op(0x55a7370f8280:1:1:2:i1:o1:s0,reindex->0x55a7371141e0)
    [i 1103 17:01:12.881178 16 v100 executor.cc:259] input: 13  addr: Op(0x55a7370f7fd0:1:1:2:i1:o1:s0,broadcast_to->0x55a7370f8080)
    [i 1103 17:01:12.881185 16 v100 executor.cc:261] 
    [i 1103 17:01:12.881191 16 v100 executor.cc:250] id: 8  type: broadcast  addr: Op(0x55a73732b060:1:1:2:i1:o1:s0,reindex->0x55a73734fa30)
    [i 1103 17:01:12.881199 16 v100 executor.cc:259] input: 14  addr: Op(0x55a73734f880:0:1:1:i0:o1:s0,curand_random->0x55a7370f2010)
    [i 1103 17:01:12.881207 16 v100 executor.cc:261] 
    [i 1103 17:01:12.881214 16 v100 executor.cc:250] id: 9  type: broadcast  addr: Op(0x55a7365d80d0:1:1:2:i1:o1:s0,broadcast_to->0x55a7365a2900)
    [i 1103 17:01:12.881221 16 v100 executor.cc:259] input: 15  addr: Op(0x55a7370ea960:0:1:1:i0:o1:s0,curand_random->0x55a7370ea850)
    [i 1103 17:01:12.881230 16 v100 executor.cc:261] 
    [i 1103 17:01:12.881237 16 v100 executor.cc:250] id: 10  type: broadcast  addr: Op(0x55a7370f0730:1:1:2:i1:o1:s0,broadcast_to->0x55a7370f07e0)
    [i 1103 17:01:12.881245 16 v100 executor.cc:259] input: 15  addr: Op(0x55a7370ea960:0:1:1:i0:o1:s0,curand_random->0x55a7370ea850)
    [i 1103 17:01:12.881252 16 v100 executor.cc:261] 
    [i 1103 17:01:12.881259 16 v100 executor.cc:250] id: 11  type: broadcast  addr: Op(0x55a7370eaa40:1:1:2:i1:o1:s0,broadcast_to->0x55a73710f2f0)
    [i 1103 17:01:12.881267 16 v100 executor.cc:259] input: 16  addr: Op(0x55a73710e200:1:1:2:i2:o1:s0,binary.multiply->0x55a736577430)
    [i 1103 17:01:12.881275 16 v100 executor.cc:261] 
    [i 1103 17:01:12.881281 16 v100 executor.cc:250] id: 12  type: broadcast  addr: Op(0x55a7370f8280:1:1:2:i1:o1:s0,reindex->0x55a7371141e0)
    [i 1103 17:01:12.881289 16 v100 executor.cc:259] input: 14  addr: Op(0x55a73734f880:0:1:1:i0:o1:s0,curand_random->0x55a7370f2010)
    [i 1103 17:01:12.881296 16 v100 executor.cc:261] 
    [i 1103 17:01:12.881303 16 v100 executor.cc:250] id: 13  type: broadcast  addr: Op(0x55a7370f7fd0:1:1:2:i1:o1:s0,broadcast_to->0x55a7370f8080)
    [i 1103 17:01:12.881311 16 v100 executor.cc:259] input: 16  addr: Op(0x55a73710e200:1:1:2:i2:o1:s0,binary.multiply->0x55a736577430)
    [i 1103 17:01:12.881319 16 v100 executor.cc:261] 
    [i 1103 17:01:12.881325 16 v100 executor.cc:250] id: 14  type: others  addr: Op(0x55a73734f880:0:1:1:i0:o1:s0,curand_random->0x55a7370f2010)
    [i 1103 17:01:12.881333 16 v100 executor.cc:261] 
    [i 1103 17:01:12.881339 16 v100 executor.cc:250] id: 15  type: others  addr: Op(0x55a7370ea960:0:1:1:i0:o1:s0,curand_random->0x55a7370ea850)
    [i 1103 17:01:12.881346 16 v100 executor.cc:261] 
    [i 1103 17:01:12.881353 16 v100 executor.cc:250] id: 16  type: element  addr: Op(0x55a73710e200:1:1:2:i2:o1:s0,binary.multiply->0x55a736577430)
    [i 1103 17:01:12.881361 16 v100 executor.cc:259] input: 5  addr: Op(0x55a737110490:0:1:1:i0:o1:s0,curand_random->0x55a73710e460)
    [i 1103 17:01:12.881370 16 v100 executor.cc:259] input: 17  addr: Op(0x55a73710e780:0:1:1:i1:o1:s0,broadcast_to->0x55a73723af20)
    [i 1103 17:01:12.881378 16 v100 executor.cc:261] 
    [i 1103 17:01:12.881384 16 v100 executor.cc:250] id: 17  type: broadcast  addr: Op(0x55a73710e780:0:1:1:i1:o1:s0,broadcast_to->0x55a73723af20)
    [i 1103 17:01:12.881392 16 v100 executor.cc:259] input: 18  addr: Op(0x55a73658eb70:0:1:1:i0:o1:s0,array->0x55a7370fe300)
    [i 1103 17:01:12.881399 16 v100 executor.cc:261] 
    [i 1103 17:01:12.881406 16 v100 executor.cc:250] id: 18  type: element  addr: Op(0x55a73658eb70:0:1:1:i0:o1:s0,array->0x55a7370fe300)
    [i 1103 17:01:12.881414 16 v100 executor.cc:261] 
    [i 1103 17:01:12.881427 16 v1000 executor.cc:426] sharegraph_q []
    [i 1103 17:01:12.881434 16 v1000 executor.cc:446] topsort internal [1,]
    [i 1103 17:01:12.881441 16 v1000 executor.cc:426] sharegraph_q []
    [i 1103 17:01:12.881448 16 v1000 executor.cc:446] topsort internal [13,12,7,3,]
    [i 1103 17:01:12.881455 16 v1000 executor.cc:426] sharegraph_q []
    [i 1103 17:01:12.881462 16 v1000 executor.cc:446] topsort internal [11,10,6,2,]
    [i 1103 17:01:12.881470 16 v1000 executor.cc:426] sharegraph_q []
    [i 1103 17:01:12.881477 16 v1000 executor.cc:446] topsort internal [9,8,4,0,]
    [i 1103 17:01:12.881484 16 v1000 executor.cc:426] sharegraph_q []
    [i 1103 17:01:12.881490 16 v1000 executor.cc:446] topsort internal [18,17,16,]
    [i 1103 17:01:12.881497 16 v1000 executor.cc:426] sharegraph_q []
    [i 1103 17:01:12.881503 16 v1000 executor.cc:446] topsort internal [15,]
    [i 1103 17:01:12.881510 16 v1000 executor.cc:426] sharegraph_q []
    [i 1103 17:01:12.881516 16 v1000 executor.cc:446] topsort internal [14,]
    [i 1103 17:01:12.881523 16 v1000 executor.cc:426] sharegraph_q []
    [i 1103 17:01:12.881529 16 v1000 executor.cc:446] topsort internal [5,]
    [i 1103 17:01:12.881537 16 v100 executor.cc:481] Run Op(0x55a737110490:0:1:1:i0:o1:s0,curand_random->0x55a73710e460)
    [i 1103 17:01:12.881547 16 v100 executor.cc:490] Run Op(0x55a737110490:0:1:1:i0:o1:s0,curand_random->0x55a73710e460) inputs: [] outputs: [Var(0x55a73710e460:1:3:3:i1:o2:s0,float32,,0x7f49b9400000)[10,5,49,49,],]
    [i 1103 17:01:12.881560 16 v100 op.cc:254] Jit op key found: curand_random[T:float32][R:uniform][JIT:1][JIT_cuda:1][index_t:int32] jit op entry: 0x7f4a0
    10,5,49,49,],]
    [i 1103 17:01:12.881615 16 v100 executor.cc:481] Run Op(0x55a73734f880:0:1:1:i0:o1:s0,curand_random->0x55a7370f2010)
    [i 1103 17:01:12.881624 16 v100 executor.cc:490] Run Op(0x55a73734f880:0:1:1:i0:o1:s0,curand_random->0x55a7370f2010) inputs: [] outputs: [Var(0x55a7370f2010:1:3:3:i1:o2:s0,float32,,0x7f49b8000000)[10,3,100,100,],]
    [i 1103 17:01:12.881635 16 v100 op.cc:254] Jit op key found: curand_random[T:float32][R:uniform][JIT:1][JIT_cuda:1][index_t:int32] jit op entry: 0x7f4a0a1e50be
    [i 1103 17:01:12.881650 16 v100 executor.cc:551] Finished Op(curand_random 1/8) output: [Var(0x55a7370f2010:1:3:3:i1:o2:s0,float32,,0x7f49b8000000)[10,3,100,100,],]
    [i 1103 17:01:12.881660 16 v100 executor.cc:481] Run Op(0x55a7370ea960:0:1:1:i0:o1:s0,curand_random->0x55a7370ea850)
    [i 1103 17:01:12.881669 16 v100 executor.cc:490] Run Op(0x55a7370ea960:0:1:1:i0:o1:s0,curand_random->0x55a7370ea850) inputs: [] outputs: [Var(0x55a7370ea850:1:3:3:i1:o2:s0,float32,,0x7f49b9475400)[5,3,3,3,],]
    [i 1103 17:01:12.881680 16 v100 op.cc:254] Jit op key found: curand_random[T:float32][R:uniform][JIT:1][JIT_cuda:1][index_t:int32] jit op entry: 0x7f4a0a1e50be
    [i 1103 17:01:12.881695 16 v100 executor.cc:551] Finished Op(curand_random 2/8) output: [Var(0x55a7370ea850:1:3:3:i1:o2:s0,float32,,0x7f49b9475400)[5,3,3,3,],]
    [i 1103 17:01:12.881706 16 v100 executor.cc:60] Prepare fused_op [Op(0x55a73658eb70:0:1:1:i0:o1:s0,array->0x55a7370fe300),Op(0x55a73710e780:0:1:1:i1:o1:s0,broadcast_to->0x55a73723af20),Op(0x55a73710e200:1:1:2:i2:o1:s0,binary.multiply->0x55a736577430),]
    [i 1103 17:01:12.881720 16 v100 executor.cc:481] Run Op(0x7ffe7aef09b0:0:0:0:i0:o1:s0,fused->0x55a736577430)
    [i 1103 17:01:12.881729 16 v100 executor.cc:490] Run Op(0x7ffe7aef09b0:0:0:0:i0:o1:s0,fused->0x55a736577430) inputs: [] outputs: [Var(0x55a736577430:1:2:3:i1:o2:s0,float32,,0x7f49b9475800)[10,5,49,49,],]
    [i 1103 17:01:12.890439 16 v100 executor.cc:551] Finished Op(fused 3/8) output: [Var(0x55a736577430:1:2:3:i1:o2:s0,float32,,0x7f49b9475800)[10,5,49,49,],]
    [i 1103 17:01:12.890475 16 v100 executor.cc:60] Prepare fused_op [Op(0x55a7365d80d0:1:1:2:i1:o1:s0,broadcast_to->0x55a7365a2900),Op(0x55a73732b060:1:1:2:i1:o1:s0,reindex->0x55a73734fa30),Op(0x55a7370fa1d0:2:1:2:i2:o1:s0,binary.multiply->0x55a7365c9ff0),Op(0x55a73712bab0:1:1:2:i1:o1:s0,reduce.add->0x55a701115d80),]
    [i 1103 17:01:12.890492 16 v100 executor.cc:481] Run Op(0x7ffe7aef09b0:0:0:0:i0:o1:s0,fused->0x55a701115d80)
    [i 1103 17:01:12.890515 16 v100 executor.cc:490] Run Op(0x7ffe7aef09b0:0:0:0:i0:o1:s0,fused->0x55a701115d80) inputs: [] outputs: [Var(0x55a701115d80:2:2:2:i1:o1:s0,float32,,0x7f49b9500000)[10,5,49,49,],]
    [i 1103 17:01:12.905278 16 v10 op.cc:258] Jit op key not found: cudnn_conv[Tx:float32][Ty:float32][Tw:float32][XFORMAT:abcd][WFORMAT:oihw][YFORMAT:abcd][JIT:1][JIT_cuda:1][index_t:int32]
    [i 1103 17:01:12.907661 16 v10 op.cc:265] Get jit op entry: 0x7f49785eff10
    [i 1103 17:01:12.907803 16 v100 executor.cc:551] Finished Op(fused 4/8) output: [Var(0x55a701115d80:2:2:2:i1:o1:s0,float32,,0x7f49b9500000)[10,5,49,49,],]
    [i 1103 17:01:12.907817 16 v100 executor.cc:60] Prepare fused_op [Op(0x55a7370eaa40:1:1:2:i1:o1:s0,broadcast_to->0x55a73710f2f0),Op(0x55a7370f0730:1:1:2:i1:o1:s0,broadcast_to->0x55a7370f07e0),Op(0x55a7370f0950:2:1:2:i2:o1:s0,binary.multiply->0x55a7370f09e0),Op(0x55a7370f8450:1:1:2:i1:o1:s0,reindex_reduce.add->0x55a7370f8530),]
    [i 1103 17:01:12.907833 16 v100 executor.cc:481] Run Op(0x7ffe7aef09b0:0:0:0:i0:o1:s0,fused->0x55a7370f8530)
    [i 1103 17:01:12.907843 16 v100 executor.cc:490] Run Op(0x7ffe7aef09b0:0:0:0:i0:o1:s0,fused->0x55a7370f8530) inputs: [] outputs: [Var(0x55a7370f8530:2:1:1:i1:o0:s0,float32,,0x7f49b8125000)[10,3,100,100,],]
    [i 1103 17:01:12.923124 16 v10 op.cc:258] Jit op key not found: cudnn_conv_backward_x[Tx:float32][Ty:float32][Tw:float32][XFORMAT:abcd][WFORMAT:oihw][YFORMAT:abcd][JIT:1][JIT_cuda:1][index_t:int32]
    [i 1103 17:01:12.925697 16 v10 op.cc:265] Get jit op entry: 0x7f49781d7100
    [i 1103 17:01:12.932590 16 v100 executor.cc:551] Finished Op(fused 5/8) output: [Var(0x55a7370f8530:2:1:1:i1:o0:s0,float32,,0x7f49b8125000)[10,3,100,100,],]
    [i 1103 17:01:12.932610 16 v100 executor.cc:60] Prepare fused_op [Op(0x55a7370f7fd0:1:1:2:i1:o1:s0,broadcast_to->0x55a7370f8080),Op(0x55a7370f8280:1:1:2:i1:o1:s0,reindex->0x55a7371141e0),Op(0x55a737114320:2:1:2:i2:o1:s0,binary.multiply->0x55a7371143b0),Op(0x55a7370f86a0:1:1:2:i1:o1:s0,reduce.add->0x55a73734ef40),]
    [i 1103 17:01:12.932627 16 v100 executor.cc:481] Run Op(0x7ffe7aef09b0:0:0:0:i0:o1:s0,fused->0x55a73734ef40)
    [i 1103 17:01:12.932636 16 v100 executor.cc:490] Run Op(0x7ffe7aef09b0:0:0:0:i0:o1:s0,fused->0x55a73734ef40) inputs: [] outputs: [Var(0x55a73734ef40:2:1:1:i1:o0:s0,float32,,0x7f49b94eac00)[5,3,3,3,],]
    [i 1103 17:01:12.951765 16 v10 op.cc:258] Jit op key not found: cudnn_conv_backward_w[Tx:float32][Ty:float32][Tw:float32][XFORMAT:abcd][WFORMAT:oihw][YFORMAT:abcd][JIT:1][JIT_cuda:1][index_t:int32]
    [i 1103 17:01:12.954082 16 v10 op.cc:265] Get jit op entry: 0x7f4945bf3438
    [i 1103 17:01:12.960545 16 v100 executor.cc:551] Finished Op(fused 6/8) output: [Var(0x55a73734ef40:2:1:1:i1:o0:s0,float32,,0x7f49b94eac00)[5,3,3,3,],]
    [i 1103 17:01:12.960566 16 v100 executor.cc:60] Prepare fused_op [Op(0x55a7370ead80:2:1:2:i2:o1:s0,binary.multiply->0x55a7365ceb70),]
    [i 1103 17:01:12.960577 16 v100 executor.cc:481] Run Op(0x7ffe7aef09b0:0:0:0:i0:o1:s0,fused->0x55a7365ceb70)
    [i 1103 17:01:12.960587 16 v100 executor.cc:490] Run Op(0x7ffe7aef09b0:0:0:0:i0:o1:s0,fused->0x55a7365ceb70) inputs: [] outputs: [Var(0x55a7365ceb70:2:1:1:i1:o0:s0,float32,,0x7f49b9575400)[10,5,49,49,],]
    [i 1103 17:01:12.981995 16 v100 executor.cc:551] Finished Op(fused 7/8) output: [Var(0x55a7365ceb70:2:1:1:i1:o0:s0,float32,,0x7f49b9575400)[10,5,49,49,],]
    [i 1103 17:01:12.982034 16 v10 executor.cc:592] All 19 ops finished, return vars: [Var(0x55a701115d80:2:2:1:i1:o1:s1,float32,,0x7f49b9500000)[10,5,49,49,],Var(0x55a7365ceb70:2:1:0:i1:o0:s1,float32,,0x7f49b9575400)[10,5,49,49,],Var(0x55a7370f8530:2:1:0:i1:o0:s1,float32,,0x7f49b8125000)[10,3,100,100,],Var(0x55a73734ef40:2:1:0:i1:o0:s1,float32,,0x7f49b94eac00)[5,3,3,3,],]
    [i 1103 17:01:12.982046 16 v10 executor.cc:606] cudaDeviceSynchronize times: 0 / 8 device_sync: 0
    [i 1103 17:01:12.982136 16 v1 cuda_flags.cc:34] CUDA disabled.
    [i 1103 17:01:12.982157 16 v10 executor.cc:592] All 0 ops finished, return vars: [Var(0x55a73734ef40:2:1:0:i1:o0:s1,float32,,0x7f49b94eac00)[5,3,3,3,],Var(0x55a7370f8530:2:1:0:i1:o0:s1,float32,,0x7f49b8125000)[10,3,100,100,],Var(0x55a7365ceb70:2:1:0:i1:o0:s1,float32,,0x7f49b9575400)[10,5,49,49,],]
    [i 1103 17:01:12.982165 16 v10 executor.cc:606] cudaDeviceSynchronize times: 0 / 0 device_sync: 0
    [i 1103 17:01:13.032396 16 cuda_flags.cc:32] CUDA enabled.
    [i 1103 17:01:13.032525 16 v100 executor.cc:250] id: 0  type: reduce  addr: Op(0x55a7370ead80:1:1:2:i1:o1:s0,reduce.add->0x55a7373379c0)
    [i 1103 17:01:13.032538 16 v100 executor.cc:259] input: 4  addr: Op(0x55a737117180:2:1:2:i2:o1:s0,binary.multiply->0x55a737134600)
    [i 1103 17:01:13.032544 16 v100 executor.cc:261] 
    [i 1103 17:01:13.032549 16 v100 executor.cc:250] id: 1  type: element  addr: Op(0x55a73734f880:2:1:2:i2:o1:s0,binary.multiply->0x55a73710e780)
    [i 1103 17:01:13.032555 16 v100 executor.cc:259] input: 5  addr: Op(0x55a737329990:0:1:1:i0:o1:s0,curand_random->0x55a701115d80)
    [i 1103 17:01:13.032561 16 v100 executor.cc:259] input: 0  addr: Op(0x55a7370ead80:1:1:2:i1:o1:s0,reduce.add->0x55a7373379c0)
    [i 1103 17:01:13.032566 16 v100 executor.cc:261] 
    [i 1103 17:01:13.032571 16 v100 executor.cc:250] id: 2  type: reduce  addr: Op(0x55a737102ad0:1:1:2:i1:o1:s0,reindex_reduce.add->0x55a7372454e0)
    [i 1103 17:01:13.032577 16 v100 executor.cc:259] input: 6  addr: Op(0x55a73710e200:2:1:2:i2:o1:s0,binary.multiply->0x55a73711ae70)
    [i 1103 17:01:13.032582 16 v100 executor.cc:261] 
    [i 1103 17:01:13.032587 16 v100 executor.cc:250] id: 3  type: reduce  addr: Op(0x55a7365ceb70:1:1:2:i1:o1:s0,reduce.add->0x55a73734ef40)
    [i 1103 17:01:13.032592 16 v100 executor.cc:259] input: 7  addr: Op(0x55a737339680:2:1:2:i2:o1:s0,binary.multiply->0x55a7371162f0)
    [i 1103 17:01:13.032598 16 v100 executor.cc:261] 
    [i 1103 17:01:13.032602 16 v100 executor.cc:250] id: 4  type: element  addr: Op(0x55a737117180:2:1:2:i2:o1:s0,binary.multiply->0x55a737134600)
    [i 1103 17:01:13.032608 16 v100 executor.cc:259] input: 8  addr: Op(0x55a73732b060:1:1:2:i1:o1:s0,reindex->0x55a7365a2900)
    [i 1103 17:01:13.032613 16 v100 executor.cc:259] input: 9  addr: Op(0x55a7365c9ff0:1:1:2:i1:o1:s0,broadcast_to->0x55a737244740)
    [i 1103 17:01:13.032621 16 v100 executor.cc:261] 
    [i 1103 17:01:13.032626 16 v100 executor.cc:250] id: 5  type: others  addr: Op(0x55a737329990:0:1:1:i0:o1:s0,curand_random->0x55a701115d80)
    [i 1103 17:01:13.032631 16 v100 executor.cc:261] 
    [i 1103 17:01:13.032636 16 v100 executor.cc:250] id: 6  type: element  addr: Op(0x55a73710e200:2:1:2:i2:o1:s0,binary.multiply->0x55a73711ae70)
    [i 1103 17:01:13.032641 16 v100 executor.cc:259] input: 10  addr: Op(0x55a7370ea850:1:1:2:i1:o1:s0,broadcast_to->0x55a737244e20)
    [i 1103 17:01:13.032646 16 v100 executor.cc:259] input: 11  addr: Op(0x55a7370ed2a0:1:1:2:i1:o1:s0,broadcast_to->0x55a737339d20)
    [i 1103 17:01:13.032652 16 v100 executor.cc:261] 
    [i 1103 17:01:13.032656 16 v100 executor.cc:250] id: 7  type: element  addr: Op(0x55a737339680:2:1:2:i2:o1:s0,binary.multiply->0x55a7371162f0)
    [i 1103 17:01:13.032661 16 v100 executor.cc:259] input: 12  addr: Op(0x55a73711c220:1:1:2:i1:o1:s0,reindex->0x55a7370f2010)
    [i 1103 17:01:13.032667 16 v100 executor.cc:259] input: 13  addr: Op(0x55a737253540:1:1:2:i1:o1:s0,broadcast_to->0x55a73711a0a0)
    [i 1103 17:01:13.032672 16 v100 executor.cc:261] 
    [i 1103 17:01:13.032676 16 v100 executor.cc:250] id: 8  type: broadcast  addr: Op(0x55a73732b060:1:1:2:i1:o1:s0,reindex->0x55a7365a2900)
    [i 1103 17:01:13.032682 16 v100 executor.cc:259] input: 14  addr: Op(0x55a73712bab0:0:1:1:i0:o1:s0,curand_random->0x55a7365d80d0)
    [i 1103 17:01:13.032687 16 v100 executor.cc:261] 
    [i 1103 17:01:13.032692 16 v100 executor.cc:250] id: 9  type: broadcast  addr: Op(0x55a7365c9ff0:1:1:2:i1:o1:s0,broadcast_to->0x55a737244740)
    [i 1103 17:01:13.032697 16 v100 executor.cc:259] input: 15  addr: Op(0x55a737110490:0:1:1:i0:o1:s0,curand_random->0x55a73734fa30)
    [i 1103 17:01:13.032703 16 v100 executor.cc:261] 
    [i 1103 17:01:13.032707 16 v100 executor.cc:250] id: 10  type: broadcast  addr: Op(0x55a7370ea850:1:1:2:i1:o1:s0,broadcast_to->0x55a737244e20)
    [i 1103 17:01:13.032712 16 v100 executor.cc:259] input: 15  addr: Op(0x55a737110490:0:1:1:i0:o1:s0,curand_random->0x55a73734fa30)
    [i 1103 17:01:13.032718 16 v100 executor.cc:261] 
    [i 1103 17:01:13.032722 16 v100 executor.cc:250] id: 11  type: broadcast  addr: Op(0x55a7370ed2a0:1:1:2:i1:o1:s0,broadcast_to->0x55a737339d20)
    [i 1103 17:01:13.032727 16 v100 executor.cc:259] input: 16  addr: Op(0x55a737114690:1:1:2:i2:o1:s0,binary.multiply->0x55a736577430)
    [i 1103 17:01:13.032733 16 v100 executor.cc:261] 
    [i 1103 17:01:13.032737 16 v100 executor.cc:250] id: 12  type: broadcast  addr: Op(0x55a73711c220:1:1:2:i1:o1:s0,reindex->0x55a7370f2010)
    [i 1103 17:01:13.032742 16 v100 executor.cc:259] input: 14  addr: Op(0x55a73712bab0:0:1:1:i0:o1:s0,curand_random->0x55a7365d80d0)
    [i 1103 17:01:13.032748 16 v100 executor.cc:261] 
    [i 1103 17:01:13.032752 16 v100 executor.cc:250] id: 13  type: broadcast  addr: Op(0x55a737253540:1:1:2:i1:o1:s0,broadcast_to->0x55a73711a0a0)
    [i 1103 17:01:13.032757 16 v100 executor.cc:259] input: 16  addr: Op(0x55a737114690:1:1:2:i2:o1:s0,binary.multiply->0x55a736577430)
    [i 1103 17:01:13.032762 16 v100 executor.cc:261] 
    [i 1103 17:01:13.032766 16 v100 executor.cc:250] id: 14  type: others  addr: Op(0x55a73712bab0:0:1:1:i0:o1:s0,curand_random->0x55a7365d80d0)
    [i 1103 17:01:13.032772 16 v100 executor.cc:261] 
    [i 1103 17:01:13.032776 16 v100 executor.cc:250] id: 15  type: others  addr: Op(0x55a737110490:0:1:1:i0:o1:s0,curand_random->0x55a73734fa30)
    [i 1103 17:01:13.032781 16 v100 executor.cc:261] 
    [i 1103 17:01:13.032786 16 v100 executor.cc:250] id: 16  type: element  addr: Op(0x55a737114690:1:1:2:i2:o1:s0,binary.multiply->0x55a736577430)
    [i 1103 17:01:13.032791 16 v100 executor.cc:259] input: 5  addr: Op(0x55a737329990:0:1:1:i0:o1:s0,curand_random->0x55a701115d80)
    [i 1103 17:01:13.032797 16 v100 executor.cc:259] input: 17  addr: Op(0x55a7370eaa40:0:1:1:i1:o1:s0,broadcast_to->0x55a73710f2f0)
    [i 1103 17:01:13.032801 16 v100 executor.cc:261] 
    [i 1103 17:01:13.032805 16 v100 executor.cc:250] id: 17  type: broadcast  addr: Op(0x55a7370eaa40:0:1:1:i1:o1:s0,broadcast_to->0x55a73710f2f0)
    [i 1103 17:01:13.032811 16 v100 executor.cc:259] input: 18  addr: Op(0x55a73658eb70:0:1:1:i0:o1:s0,array->0x55a7370fe300)
    [i 1103 17:01:13.032817 16 v100 executor.cc:261] 
    [i 1103 17:01:13.032821 16 v100 executor.cc:250] id: 18  type: element  addr: Op(0x55a73658eb70:0:1:1:i0:o1:s0,array->0x55a7370fe300)
    [i 1103 17:01:13.032826 16 v100 executor.cc:261] 
    [i 1103 17:01:13.032835 16 v1000 executor.cc:426] sharegraph_q []
    [i 1103 17:01:13.032840 16 v1000 executor.cc:446] topsort internal [1,]
    [i 1103 17:01:13.032845 16 v1000 executor.cc:426] sharegraph_q []
    [i 1103 17:01:13.032850 16 v1000 executor.cc:446] topsort internal [13,12,7,3,]
    [i 1103 17:01:13.032855 16 v1000 executor.cc:426] sharegraph_q []
    [i 1103 17:01:13.032859 16 v1000 executor.cc:446] topsort internal [11,10,6,2,]
    [i 1103 17:01:13.032865 16 v1000 executor.cc:426] sharegraph_q []
    [i 1103 17:01:13.032870 16 v1000 executor.cc:446] topsort internal [9,8,4,0,]
    [i 1103 17:01:13.032874 16 v1000 executor.cc:426] sharegraph_q []
    [i 1103 17:01:13.032879 16 v1000 executor.cc:446] topsort internal [18,17,16,]
    [i 1103 17:01:13.032883 16 v1000 executor.cc:426] sharegraph_q []
    [i 1103 17:01:13.032888 16 v1000 executor.cc:446] topsort internal [15,]
    [i 1103 17:01:13.032892 16 v1000 executor.cc:426] sharegraph_q []
    [i 1103 17:01:13.032896 16 v1000 executor.cc:446] topsort internal [14,]
    [i 1103 17:01:13.032901 16 v1000 executor.cc:426] sharegraph_q []
    [i 1103 17:01:13.032905 16 v1000 executor.cc:446] topsort internal [5,]
    [i 1103 17:01:13.032910 16 v100 executor.cc:481] Run Op(0x55a737329990:0:1:1:i0:o1:s0,curand_random->0x55a701115d80)
    [i 1103 17:01:13.032917 16 v100 executor.cc:490] Run Op(0x55a737329990:0:1:1:i0:o1:s0,curand_random->0x55a701115d80) inputs: [] outputs: [Var(0x55a701115d80:1:3:3:i1:o2:s0,float32,,0x7f49b9400000)[10,5,38,48,],]
    [i 1103 17:01:13.032927 16 v100 op.cc:254] Jit op key found: curand_random[T:float32][R:uniform][JIT:1][JIT_cuda:1][index_t:int32] jit op entry: 0x7f4a0a1e50be
    [i 1103 17:01:13.032971 16 v100 executor.cc:551] Finished Op(curand_random 0/8) output: [Var(0x55a701115d80:1:3:3:i1:o2:s0,float32,,0x7f49b9400000)[10,5,38,48,],]
    [i 1103 17:01:13.032979 16 v100 executor.cc:481] Run Op(0x55a73712bab0:0:1:1:i0:o1:s0,curand_random->0x55a7365d80d0)
    [i 1103 17:01:13.032984 16 v100 executor.cc:490] Run Op(0x55a73712bab0:0:1:1:i0:o1:s0,curand_random->0x55a7365d80d0) inputs: [] outputs: [Var(0x55a7365d80d0:1:3:3:i1:o2:s0,float32,,0x7f49b9459200)[10,4,40,50,],]
    [i 1103 17:01:13.032991 16 v100 op.cc:254] Jit op key found: curand_random[T:float32][R:uniform][JIT:1][JIT_cuda:1][index_t:int32] jit op entry: 0x7f4a0a1e50be
    [i 1103 17:01:13.033001 16 v100 executor.cc:551] Finished Op(curand_random 1/8) output: [Var(0x55a7365d80d0:1:3:3:i1:o2:s0,float32,,0x7f49b9459200)[10,4,40,50,],]
    [i 1103 17:01:13.033007 16 v100 executor.cc:481] Run Op(0x55a737110490:0:1:1:i0:o1:s0,curand_random->0x55a73734fa30)
    [i 1103 17:01:13.033013 16 v100 executor.cc:490] Run Op(0x55a737110490:0:1:1:i0:o1:s0,curand_random->0x55a73734fa30) inputs: [] outputs: [Var(0x55a73734fa30:1:3:3:i1:o2:s0,float32,,0x7f49b94a7400)[5,4,5,5,],]
    [i 1103 17:01:13.033020 16 v100 op.cc:254] Jit op key found: curand_random[T:float32][R:uniform][JIT:1][JIT_cuda:1][index_t:int32] jit op entry: 0x7f4a0a1e50be
    [i 1103 17:01:13.033027 16 v100 executor.cc:551] Finished Op(curand_random 2/8) output: [Var(0x55a73734fa30:1:3:3:i1:o2:s0,float32,,0x7f49b94a7400)[5,4,5,5,],]
    [i 1103 17:01:13.033034 16 v100 executor.cc:60] Prepare fused_op [Op(0x55a73658eb70:0:1:1:i0:o1:s0,array->0x55a7370fe300),Op(0x55a7370eaa40:0:1:1:i1:o1:s0,broadcast_to->0x55a73710f2f0),Op(0x55a737114690:1:1:2:i2:o1:s0,binary.multiply->0x55a736577430),]
    [i 1103 17:01:13.033044 16 v100 executor.cc:481] Run Op(0x7ffe7aef09b0:0:0:0:i0:o1:s0,fused->0x55a736577430)
    [i 1103 17:01:13.033050 16 v100 executor.cc:490] Run Op(0x7ffe7aef09b0:0:0:0:i0:o1:s0,fused->0x55a736577430) inputs: [] outputs: [Var(0x55a736577430:1:2:3:i1:o2:s0,float32,,0x7f49b9500000)[10,5,38,48,],]
    [i 1103 17:01:13.033074 16 v100 executor.cc:551] Finished Op(fused 3/8) output: [Var(0x55a736577430:1:2:3:i1:o2:s0,float32,,0x7f49b9500000)[10,5,38,48,],]
    [i 1103 17:01:13.033081 16 v100 executor.cc:60] Prepare fused_op [Op(0x55a7365c9ff0:1:1:2:i1:o1:s0,broadcast_to->0x55a737244740),Op(0x55a73732b060:1:1:2:i1:o1:s0,reindex->0x55a7365a2900),Op(0x55a737117180:2:1:2:i2:o1:s0,binary.multiply->0x55a737134600),Op(0x55a7370ead80:1:1:2:i1:o1:s0,reduce.add->0x55a7373379c0),]
    [i 1103 17:01:13.033092 16 v100 executor.cc:481] Run Op(0x7ffe7aef09b0:0:0:0:i0:o1:s0,fused->0x55a7373379c0)
    [i 1103 17:01:13.033098 16 v100 executor.cc:490] Run Op(0x7ffe7aef09b0:0:0:0:i0:o1:s0,fused->0x55a7373379c0) inputs: [] outputs: [Var(0x55a7373379c0:2:2:2:i1:o1:s0,float32,,0x7f49b9559200)[10,5,38,48,],]
    [i 1103 17:01:13.046859 16 v100 op.cc:254] Jit op key found: cudnn_conv[Tx:float32][Ty:float32][Tw:float32][XFORMAT:abcd][WFORMAT:oihw][YFORMAT:abcd][JIT:1][JIT_cuda:1][index_t:int32] jit op entry: 0x7f49785eff10
    [i 1103 17:01:13.046962 16 v100 executor.cc:551] Finished Op(fused 4/8) output: [Var(0x55a7373379c0:2:2:2:i1:o1:s0,float32,,0x7f49b9559200)[10,5,38,48,],]
    [i 1103 17:01:13.046971 16 v100 executor.cc:60] Prepare fused_op [Op(0x55a7370ed2a0:1:1:2:i1:o1:s0,broadcast_to->0x55a737339d20),Op(0x55a7370ea850:1:1:2:i1:o1:s0,broadcast_to->0x55a737244e20),Op(0x55a73710e200:2:1:2:i2:o1:s0,binary.multiply->0x55a73711ae70),Op(0x55a737102ad0:1:1:2:i1:o1:s0,reindex_reduce.add->0x55a7372454e0),]
    [i 1103 17:01:13.046983 16 v100 executor.cc:481] Run Op(0x7ffe7aef09b0:0:0:0:i0:o1:s0,fused->0x55a7372454e0)
    [i 1103 17:01:13.046991 16 v100 executor.cc:490] Run Op(0x7ffe7aef09b0:0:0:0:i0:o1:s0,fused->0x55a7372454e0) inputs: [] outputs: [Var(0x55a7372454e0:2:1:1:i1:o0:s0,float32,,0x7f49b94a7c00)[10,4,40,50,],]
    [i 1103 17:01:13.060729 16 v100 op.cc:254] Jit op key found: cudnn_conv_backward_x[Tx:float32][Ty:float32][Tw:float32][XFORMAT:abcd][WFORMAT:oihw][YFORMAT:abcd][JIT:1][JIT_cuda:1][index_t:int32] jit op entry: 0x7f49781d7100
    [i 1103 17:01:13.074692 16 v100 executor.cc:551] Finished Op(fused 5/8) output: [Var(0x55a7372454e0:2:1:1:i1:o0:s0,float32,,0x7f49b94a7c00)[10,4,40,50,],]
    [i 1103 17:01:13.074734 16 v100 executor.cc:60] Prepare fused_op [Op(0x55a737253540:1:1:2:i1:o1:s0,broadcast_to->0x55a73711a0a0),Op(0x55a73711c220:1:1:2:i1:o1:s0,reindex->0x55a7370f2010),Op(0x55a737339680:2:1:2:i2:o1:s0,binary.multiply->0x55a7371162f0),Op(0x55a7365ceb70:1:1:2:i1:o1:s0,reduce.add->0x55a73734ef40),]
    [i 1103 17:01:13.074758 16 v100 executor.cc:481] Run Op(0x7ffe7aef09b0:0:0:0:i0:o1:s0,fused->0x55a73734ef40)
    [i 1103 17:01:13.074771 16 v100 executor.cc:490] Run Op(0x7ffe7aef09b0:0:0:0:i0:o1:s0,fused->0x55a73734ef40) inputs: [] outputs: [Var(0x55a73734ef40:2:1:1:i1:o0:s0,float32,,0x7f49b94f5e00)[5,4,5,5,],]
    [i 1103 17:01:13.094181 16 v100 op.cc:254] Jit op key found: cudnn_conv_backward_w[Tx:float32][Ty:float32][Tw:float32][XFORMAT:abcd][WFORMAT:oihw][YFORMAT:abcd][JIT:1][JIT_cuda:1][index_t:int32] jit op entry: 0x7f4945bf3438
    [i 1103 17:01:13.109275 16 v100 executor.cc:551] Finished Op(fused 6/8) output: [Var(0x55a73734ef40:2:1:1:i1:o0:s0,float32,,0x7f49b94f5e00)[5,4,5,5,],]
    [i 1103 17:01:13.109301 16 v100 executor.cc:60] Prepare fused_op [Op(0x55a73734f880:2:1:2:i2:o1:s0,binary.multiply->0x55a73710e780),]
    [i 1103 17:01:13.109316 16 v100 executor.cc:481] Run Op(0x7ffe7aef09b0:0:0:0:i0:o1:s0,fused->0x55a73710e780)
    [i 1103 17:01:13.110649 16 v100 executor.cc:490] Run Op(0x7ffe7aef09b0:0:0:0:i0:o1:s0,fused->0x55a73710e780) inputs: [] outputs: [Var(0x55a73710e780:2:1:1:i1:o0:s0,float32,,0x7f4946800000)[10,5,38,48,],]
    [i 1103 17:01:13.110696 16 v100 executor.cc:551] Finished Op(fused 7/8) output: [Var(0x55a73710e780:2:1:1:i1:o0:s0,float32,,0x7f4946800000)[10,5,38,48,],]
    [i 1103 17:01:13.110711 16 v10 executor.cc:592] All 19 ops finished, return vars: [Var(0x55a7373379c0:2:2:1:i1:o1:s1,float32,,0x7f49b9559200)[10,5,38,48,],Var(0x55a73710e780:2:1:0:i1:o0:s1,float32,,0x7f4946800000)[10,5,38,48,],Var(0x55a7372454e0:2:1:0:i1:o0:s1,float32,,0x7f49b94a7c00)[10,4,40,50,],Var(0x55a73734ef40:2:1:0:i1:o0:s1,float32,,0x7f49b94f5e00)[5,4,5,5,],]
    [i 1103 17:01:13.110732 16 v10 executor.cc:606] cudaDeviceSynchronize times: 0 / 8 device_sync: 0
    [i 1103 17:01:13.110776 16 v1 cuda_flags.cc:34] CUDA disabled.
    [i 1103 17:01:13.110796 16 v10 executor.cc:592] All 0 ops finished, return vars: [Var(0x55a73734ef40:2:1:0:i1:o0:s1,float32,,0x7f49b94f5e00)[5,4,5,5,],Var(0x55a7372454e0:2:1:0:i1:o0:s1,float32,,0x7f49b94a7c00)[10,4,40,50,],Var(0x55a73710e780:2:1:0:i1:o0:s1,float32,,0x7f4946800000)[10,5,38,48,],]
    [i 1103 17:01:13.110808 16 v10 executor.cc:606] cudaDeviceSynchronize times: 0 / 0 device_sync: 0
    [i 1103 17:01:13.184799 16 cuda_flags.cc:32] CUDA enabled.
    [i 1103 17:01:13.184934 16 v100 executor.cc:250] id: 0  type: reduce  addr: Op(0x55a73712bab0:1:1:2:i1:o1:s0,reduce.add->0x55a7373379c0)
    [i 1103 17:01:13.184949 16 v100 executor.cc:259] input: 4  addr: Op(0x55a737110490:2:1:2:i2:o1:s0,binary.multiply->0x55a737253bf0)
    [i 1103 17:01:13.184956 16 v100 executor.cc:261] 
    [i 1103 17:01:13.184961 16 v100 executor.cc:250] id: 1  type: element  addr: Op(0x55a73710e200:2:1:2:i2:o1:s0,binary.multiply->0x55a7370fe300)
    [i 1103 17:01:13.184967 16 v100 executor.cc:259] input: 5  addr: Op(0x55a737114690:0:1:1:i0:o1:s0,curand_random->0x55a7370eaa40)
    [i 1103 17:01:13.184973 16 v100 executor.cc:259] input: 0  addr: Op(0x55a73712bab0:1:1:2:i1:o1:s0,reduce.add->0x55a7373379c0)
    [i 1103 17:01:13.184979 16 v100 executor.cc:261] 
    [i 1103 17:01:13.184983 16 v100 executor.cc:250] id: 2  type: reduce  addr: Op(0x55a737331780:1:1:2:i1:o1:s0,reindex_reduce.add->0x55a73734fa30)
    [i 1103 17:01:13.184989 16 v100 executor.cc:259] input: 6  addr: Op(0x55a7365ceb70:2:1:2:i2:o1:s0,binary.multiply->0x55a7371162f0)
    [i 1103 17:01:13.184995 16 v100 executor.cc:261] 
    [i 1103 17:01:13.184999 16 v100 executor.cc:250] id: 3  type: reduce  addr: Op(0x55a737117180:1:1:2:i1:o1:s0,reduce.add->0x55a7370f86a0)
    [i 1103 17:01:13.185005 16 v100 executor.cc:259] input: 7  addr: Op(0x55a737353850:2:1:2:i2:o1:s0,binary.multiply->0x55a737113b30)
    [i 1103 17:01:13.185010 16 v100 executor.cc:261] 
    [i 1103 17:01:13.185014 16 v100 executor.cc:250] id: 4  type: element  addr: Op(0x55a737110490:2:1:2:i2:o1:s0,binary.multiply->0x55a737253bf0)
    [i 1103 17:01:13.185020 16 v100 executor.cc:259] input: 8  addr: Op(0x55a73732b060:1:1:2:i1:o1:s0,reindex->0x55a737244740)
    [i 1103 17:01:13.185026 16 v100 executor.cc:259] input: 9  addr: Op(0x55a737134600:1:1:2:i1:o1:s0,broadcast_to->0x55a73710e460)
    [i 1103 17:01:13.185033 16 v100 executor.cc:261] 
    [i 1103 17:01:13.185037 16 v100 executor.cc:250] id: 5  type: others  addr: Op(0x55a737114690:0:1:1:i0:o1:s0,curand_random->0x55a7370eaa40)
    [i 1103 17:01:13.185043 16 v100 executor.cc:261] 
    [i 1103 17:01:13.185047 16 v100 executor.cc:250] id: 6  type: element  addr: Op(0x55a7365ceb70:2:1:2:i2:o1:s0,binary.multiply->0x55a7371162f0)
    [i 1103 17:01:13.185053 16 v100 executor.cc:259] input: 10  addr: Op(0x55a7370f2010:1:1:2:i1:o1:s0,broadcast_to->0x55a73711a0a0)
    [i 1103 17:01:13.185059 16 v100 executor.cc:259] input: 11  addr: Op(0x55a737253540:1:1:2:i1:o1:s0,broadcast_to->0x55a736577430)
    [i 1103 17:01:13.185065 16 v100 executor.cc:261] 
    [i 1103 17:01:13.185069 16 v100 executor.cc:250] id: 7  type: element  addr: Op(0x55a737353850:2:1:2:i2:o1:s0,binary.multiply->0x55a737113b30)
    [i 1103 17:01:13.185074 16 v100 executor.cc:259] input: 12  addr: Op(0x55a737328490:1:1:2:i1:o1:s0,reindex->0x55a73734f0d0)
    [i 1103 17:01:13.185080 16 v100 executor.cc:259] input: 13  addr: Op(0x55a737103240:1:1:2:i1:o1:s0,broadcast_to->0x55a7370ebe60)
    [i 1103 17:01:13.185085 16 v100 executor.cc:261] 
    [i 1103 17:01:13.185089 16 v100 executor.cc:250] id: 8  type: broadcast  addr: Op(0x55a73732b060:1:1:2:i1:o1:s0,reindex->0x55a737244740)
    [i 1103 17:01:13.185094 16 v100 executor.cc:259] input: 14  addr: Op(0x55a7370ea960:0:1:1:i0:o1:s0,curand_random->0x55a7365c9ff0)
    [i 1103 17:01:13.185099 16 v100 executor.cc:261] 
    [i 1103 17:01:13.185104 16 v100 executor.cc:250] id: 9  type: broadcast  addr: Op(0x55a737134600:1:1:2:i1:o1:s0,broadcast_to->0x55a73710e460)
    [i 1103 17:01:13.185110 16 v100 executor.cc:259] input: 15  addr: Op(0x55a7370ead80:0:1:1:i0:o1:s0,curand_random->0x55a7365a2900)
    [i 1103 17:01:13.185116 16 v100 executor.cc:261] 
    [i 1103 17:01:13.185120 16 v100 executor.cc:250] id: 10  type: broadcast  addr: Op(0x55a7370f2010:1:1:2:i1:o1:s0,broadcast_to->0x55a73711a0a0)
    [i 1103 17:01:13.185126 16 v100 executor.cc:259] input: 15  addr: Op(0x55a7370ead80:0:1:1:i0:o1:s0,curand_random->0x55a7365a2900)
    [i 1103 17:01:13.185131 16 v100 executor.cc:261] 
    [i 1103 17:01:13.185135 16 v100 executor.cc:250] id: 11  type: broadcast  addr: Op(0x55a737253540:1:1:2:i1:o1:s0,broadcast_to->0x55a736577430)
    [i 1103 17:01:13.185140 16 v100 executor.cc:259] input: 16  addr: Op(0x55a737339680:1:1:2:i2:o1:s0,binary.multiply->0x55a737244e20)
    [i 1103 17:01:13.185145 16 v100 executor.cc:261] 
    [i 1103 17:01:13.185149 16 v100 executor.cc:250] id: 12  type: broadcast  addr: Op(0x55a737328490:1:1:2:i1:o1:s0,reindex->0x55a73734f0d0)
    [i 1103 17:01:13.185155 16 v100 executor.cc:259] input: 14  addr: Op(0x55a7370ea960:0:1:1:i0:o1:s0,curand_random->0x55a7365c9ff0)
    [i 1103 17:01:13.185160 16 v100 executor.cc:261] 
    [i 1103 17:01:13.185165 16 v100 executor.cc:250] id: 13  type: broadcast  addr: Op(0x55a737103240:1:1:2:i1:o1:s0,broadcast_to->0x55a7370ebe60)
    [i 1103 17:01:13.218807 16 v100 executor.cc:259] input: 16  addr: Op(0x55a737339680:1:1:2:i2:o1:s0,binary.multiply->0x55a737244e20)
    [i 1103 17:01:13.218854 16 v100 executor.cc:261] 
    [i 1103 17:01:13.218867 16 v100 executor.cc:250] id: 14  type: others  addr: Op(0x55a7370ea960:0:1:1:i0:o1:s0,curand_random->0x55a7365c9ff0)
    [i 1103 17:01:13.218884 16 v100 executor.cc:261] 
    [i 1103 17:01:13.218896 16 v100 executor.cc:250] id: 15  type: others  addr: Op(0x55a7370ead80:0:1:1:i0:o1:s0,curand_random->0x55a7365a2900)
    [i 1103 17:01:13.218910 16 v100 executor.cc:261] 
    [i 1103 17:01:13.219168 16 v100 executor.cc:250] id: 16  type: element  addr: Op(0x55a737339680:1:1:2:i2:o1:s0,binary.multiply->0x55a737244e20)
    [i 1103 17:01:13.219189 16 v100 executor.cc:259] input: 5  addr: Op(0x55a737114690:0:1:1:i0:o1:s0,curand_random->0x55a7370eaa40)
    [i 1103 17:01:13.219203 16 v100 executor.cc:259] input: 17  addr: Op(0x55a7370ea850:0:1:1:i1:o1:s0,broadcast_to->0x55a7370ed2a0)
    [i 1103 17:01:13.219218 16 v100 executor.cc:261] 
    [i 1103 17:01:13.219229 16 v100 executor.cc:250] id: 17  type: broadcast  addr: Op(0x55a7370ea850:0:1:1:i1:o1:s0,broadcast_to->0x55a7370ed2a0)
    [i 1103 17:01:13.219246 16 v100 executor.cc:259] input: 18  addr: Op(0x55a737276ba0:0:1:1:i0:o1:s0,array->0x55a73710f2f0)
    [i 1103 17:01:13.219261 16 v100 executor.cc:261] 
    [i 1103 17:01:13.219273 16 v100 executor.cc:250] id: 18  type: element  addr: Op(0x55a737276ba0:0:1:1:i0:o1:s0,array->0x55a73710f2f0)
    [i 1103 17:01:13.219288 16 v100 executor.cc:261] 
    [i 1103 17:01:13.219323 16 v1000 executor.cc:426] sharegraph_q []
    [i 1103 17:01:13.219337 16 v1000 executor.cc:446] topsort internal [1,]
    [i 1103 17:01:13.219349 16 v1000 executor.cc:426] sharegraph_q []
    [i 1103 17:01:13.219360 16 v1000 executor.cc:446] topsort internal [13,12,7,3,]
    [i 1103 17:01:13.219372 16 v1000 executor.cc:426] sharegraph_q []
    [i 1103 17:01:13.219383 16 v1000 executor.cc:446] topsort internal [11,10,6,2,]
    [i 1103 17:01:13.219401 16 v1000 executor.cc:426] sharegraph_q []
    [i 1103 17:01:13.219412 16 v1000 executor.cc:446] topsort internal [9,8,4,0,]
    [i 1103 17:01:13.219425 16 v1000 executor.cc:426] sharegraph_q []
    [i 1103 17:01:13.219436 16 v1000 executor.cc:446] topsort internal [18,17,16,]
    [i 1103 17:01:13.219447 16 v1000 executor.cc:426] sharegraph_q []
    [i 1103 17:01:13.219458 16 v1000 executor.cc:446] topsort internal [15,]
    [i 1103 17:01:13.219469 16 v1000 executor.cc:426] sharegraph_q []
    [i 1103 17:01:13.219480 16 v1000 executor.cc:446] topsort internal [14,]
    [i 1103 17:01:13.219490 16 v1000 executor.cc:426] sharegraph_q []
    [i 1103 17:01:13.219501 16 v1000 executor.cc:446] topsort internal [5,]
    [i 1103 17:01:13.219516 16 v100 executor.cc:481] Run Op(0x55a737114690:0:1:1:i0:o1:s0,curand_random->0x55a7370eaa40)
    [i 1103 17:01:13.219537 16 v100 executor.cc:490] Run Op(0x55a737114690:0:1:1:i0:o1:s0,curand_random->0x55a7370eaa40) inputs: [] outputs: [Var(0x55a7370eaa40:1:3:3:i1:o2:s0,float32,,0x7f49b9400000)[10,5,13,17,],]
    [i 1103 17:01:13.219562 16 v100 op.cc:254] Jit op key found: curand_random[T:float32][R:uniform][JIT:1][JIT_cuda:1][index_t:int32] jit op entry: 0x7f4a0a1e50be
    [i 1103 17:01:13.219627 16 v100 executor.cc:551] Finished Op(curand_random 0/8) output: [Var(0x55a7370eaa40:1:3:3:i1:o2:s0,float32,,0x7f49b9400000)[10,5,13,17,],]
    [i 1103 17:01:13.219663 16 v100 executor.cc:481] Run Op(0x55a7370ea960:0:1:1:i0:o1:s0,curand_random->0x55a7365c9ff0)
    [i 1103 17:01:13.219679 16 v100 executor.cc:490] Run Op(0x55a7370ea960:0:1:1:i0:o1:s0,curand_random->0x55a7365c9ff0) inputs: [] outputs: [Var(0x55a7365c9ff0:1:3:3:i1:o2:s0,float32,,0x7f49b940ae00)[10,4,40,50,],]
    [i 1103 17:01:13.219698 16 v100 op.cc:254] Jit op key found: curand_random[T:float32][R:uniform][JIT:1][JIT_cuda:1][index_t:int32] jit op entry: 0x7f4a0a1e50be
    [i 1103 17:01:13.219723 16 v100 executor.cc:551] Finished Op(curand_random 1/8) output: [Var(0x55a7365c9ff0:1:3:3:i1:o2:s0,float32,,0x7f49b940ae00)[10,4,40,50,],]
    [i 1103 17:01:13.219741 16 v100 executor.cc:481] Run Op(0x55a7370ead80:0:1:1:i0:o1:s0,curand_random->0x55a7365a2900)
    [i 1103 17:01:13.219754 16 v100 executor.cc:490] Run Op(0x55a7370ead80:0:1:1:i0:o1:s0,curand_random->0x55a7365a2900) inputs: [] outputs: [Var(0x55a7365a2900:1:3:3:i1:o2:s0,float32,,0x7f49b9459000)[5,4,4,4,],]
    [i 1103 17:01:13.219773 16 v100 op.cc:254] Jit op key found: curand_random[T:float32][R:uniform][JIT:1][JIT_cuda:1][index_t:int32] jit op entry: 0x7f4a0a1e50be
    [i 1103 17:01:13.219795 16 v100 executor.cc:551] Finished Op(curand_random 2/8) output: [Var(0x55a7365a2900:1:3:3:i1:o2:s0,float32,,0x7f49b9459000)[5,4,4,4,],]
    [i 1103 17:01:13.219813 16 v100 executor.cc:60] Prepare fused_op [Op(0x55a737276ba0:0:1:1:i0:o1:s0,array->0x55a73710f2f0),Op(0x55a7370ea850:0:1:1:i1:o1:s0,broadcast_to->0x55a7370ed2a0),Op(0x55a737339680:1:1:2:i2:o1:s0,binary.multiply->0x55a737244e20),]
    [i 1103 17:01:13.219837 16 v100 executor.cc:481] Run Op(0x7ffe7aef09b0:0:0:0:i0:o1:s0,fused->0x55a737244e20)
    [i 1103 17:01:13.219852 16 v100 executor.cc:490] Run Op(0x7ffe7aef09b0:0:0:0:i0:o1:s0,fused->0x55a737244e20) inputs: [] outputs: [Var(0x55a737244e20:1:2:3:i1:o2:s0,float32,,0x7f49b9459600)[10,5,13,17,],]
    [i 1103 17:01:13.219896 16 v100 executor.cc:551] Finished Op(fused 3/8) output: [Var(0x55a737244e20:1:2:3:i1:o2:s0,float32,,0x7f49b9459600)[10,5,13,17,],]
    [i 1103 17:01:13.219915 16 v100 executor.cc:60] Prepare fused_op [Op(0x55a737134600:1:1:2:i1:o1:s0,broadcast_to->0x55a73710e460),Op(0x55a73732b060:1:1:2:i1:o1:s0,reindex->0x55a737244740),Op(0x55a737110490:2:1:2:i2:o1:s0,binary.multiply->0x55a737253bf0),Op(0x55a73712bab0:1:1:2:i1:o1:s0,reduce.add->0x55a7373379c0),]
    [i 1103 17:01:13.219940 16 v100 executor.cc:481] Run Op(0x7ffe7aef09b0:0:0:0:i0:o1:s0,fused->0x55a7373379c0)
    [i 1103 17:01:13.219955 16 v100 executor.cc:490] Run Op(0x7ffe7aef09b0:0:0:0:i0:o1:s0,fused->0x55a7373379c0) inputs: [] outputs: [Var(0x55a7373379c0:2:2:2:i1:o1:s0,float32,,0x7f49b9464400)[10,5,13,17,],]
    [i 1103 17:01:13.243395 16 v100 op.cc:254] Jit op key found: cudnn_conv[Tx:float32][Ty:float32][Tw:float32][XFORMAT:abcd][WFORMAT:oihw][YFORMAT:abcd][JIT:1][JIT_cuda:1][index_t:int32] jit op entry: 0x7f49785eff10
    [i 1103 17:01:13.243493 16 v100 executor.cc:551] Finished Op(fused 4/8) output: [Var(0x55a7373379c0:2:2:2:i1:o1:s0,float32,,0x7f49b9464400)[10,5,13,17,],]
    [i 1103 17:01:13.243503 16 v100 executor.cc:60] Prepare fused_op [Op(0x55a737253540:1:1:2:i1:o1:s0,broadcast_to->0x55a736577430),Op(0x55a7370f2010:1:1:2:i1:o1:s0,broadcast_to->0x55a73711a0a0),Op(0x55a7365ceb70:2:1:2:i2:o1:s0,binary.multiply->0x55a7371162f0),Op(0x55a737331780:1:1:2:i1:o1:s0,reindex_reduce.add->0x55a73734fa30),]
    [i 1103 17:01:13.243515 16 v100 executor.cc:481] Run Op(0x7ffe7aef09b0:0:0:0:i0:o1:s0,fused->0x55a73734fa30)
    [i 1103 17:01:13.243523 16 v100 executor.cc:490] Run Op(0x7ffe7aef09b0:0:0:0:i0:o1:s0,fused->0x55a73734fa30) inputs: [] outputs: [Var(0x55a73734fa30:2:1:1:i1:o0:s0,float32,,0x7f49b946f200)[10,4,40,50,],]
    [i 1103 17:01:13.257515 16 v100 op.cc:254] Jit op key found: cudnn_conv_backward_x[Tx:float32][Ty:float32][Tw:float32][XFORMAT:abcd][WFORMAT:oihw][YFORMAT:abcd][JIT:1][JIT_cuda:1][index_t:int32] jit op entry: 0x7f49781d7100
    [i 1103 17:01:13.261385 16 v100 executor.cc:551] Finished Op(fused 5/8) output: [Var(0x55a73734fa30:2:1:1:i1:o0:s0,float32,,0x7f49b946f200)[10,4,40,50,],]
    [i 1103 17:01:13.261412 16 v100 executor.cc:60] Prepare fused_op [Op(0x55a737103240:1:1:2:i1:o1:s0,broadcast_to->0x55a7370ebe60),Op(0x55a737328490:1:1:2:i1:o1:s0,reindex->0x55a73734f0d0),Op(0x55a737353850:2:1:2:i2:o1:s0,binary.multiply->0x55a737113b30),Op(0x55a737117180:1:1:2:i1:o1:s0,reduce.add->0x55a7370f86a0),]
    [i 1103 17:01:13.261431 16 v100 executor.cc:481] Run Op(0x7ffe7aef09b0:0:0:0:i0:o1:s0,fused->0x55a7370f86a0)
    [i 1103 17:01:13.261442 16 v100 executor.cc:490] Run Op(0x7ffe7aef09b0:0:0:0:i0:o1:s0,fused->0x55a7370f86a0) inputs: [] outputs: [Var(0x55a7370f86a0:2:1:1:i1:o0:s0,float32,,0x7f49b94bd400)[5,4,4,4,],]
    [i 1103 17:01:13.279361 16 v100 op.cc:254] Jit op key found: cudnn_conv_backward_w[Tx:float32][Ty:float32][Tw:float32][XFORMAT:abcd][WFORMAT:oihw][YFORMAT:abcd][JIT:1][JIT_cuda:1][index_t:int32] jit op entry: 0x7f4945bf3438
    [i 1103 17:01:13.287683 16 v100 executor.cc:551] Finished Op(fused 6/8) output: [Var(0x55a7370f86a0:2:1:1:i1:o0:s0,float32,,0x7f49b94bd400)[5,4,4,4,],]
    [i 1103 17:01:13.287716 16 v100 executor.cc:60] Prepare fused_op [Op(0x55a73710e200:2:1:2:i2:o1:s0,binary.multiply->0x55a7370fe300),]
    [i 1103 17:01:13.287730 16 v100 executor.cc:481] Run Op(0x7ffe7aef09b0:0:0:0:i0:o1:s0,fused->0x55a7370fe300)
    [i 1103 17:01:13.287742 16 v100 executor.cc:490] Run Op(0x7ffe7aef09b0:0:0:0:i0:o1:s0,fused->0x55a7370fe300) inputs: [] outputs: [Var(0x55a7370fe300:2:1:1:i1:o0:s0,float32,,0x7f49b94bda00)[10,5,13,17,],]
    [i 1103 17:01:13.291295 16 v100 executor.cc:551] Finished Op(fused 7/8) output: [Var(0x55a7370fe300:2:1:1:i1:o0:s0,float32,,0x7f49b94bda00)[10,5,13,17,],]
    [i 1103 17:01:13.291313 16 v10 executor.cc:592] All 19 ops finished, return vars: [Var(0x55a7373379c0:2:2:1:i1:o1:s1,float32,,0x7f49b9464400)[10,5,13,17,],Var(0x55a7370fe300:2:1:0:i1:o0:s1,float32,,0x7f49b94bda00)[10,5,13,17,],Var(0x55a73734fa30:2:1:0:i1:o0:s1,float32,,0x7f49b946f200)[10,4,40,50,],Var(0x55a7370f86a0:2:1:0:i1:o0:s1,float32,,0x7f49b94bd400)[5,4,4,4,],]
    [i 1103 17:01:13.291327 16 v10 executor.cc:606] cudaDeviceSynchronize times: 0 / 8 device_sync: 0
    [i 1103 17:01:13.291421 16 v1 cuda_flags.cc:34] CUDA disabled.
    [i 1103 17:01:13.291444 16 v10 executor.cc:592] All 0 ops finished, return vars: [Var(0x55a7370f86a0:2:1:0:i1:o0:s1,float32,,0x7f49b94bd400)[5,4,4,4,],Var(0x55a73734fa30:2:1:0:i1:o0:s1,float32,,0x7f49b946f200)[10,4,40,50,],Var(0x55a7370fe300:2:1:0:i1:o0:s1,float32,,0x7f49b94bda00)[10,5,13,17,],]
    [i 1103 17:01:13.291455 16 v10 executor.cc:606] cudaDeviceSynchronize times: 0 / 0 device_sync: 0
    ..[i 1103 17:01:13.319210 16 cuda_flags.cc:32] CUDA enabled.
    [i 1103 17:01:13.425845 16 cuda_flags.cc:32] CUDA enabled.
    [i 1103 17:01:13.493165 16 cuda_flags.cc:32] CUDA enabled.
    [i 1103 17:01:13.561802 16 cuda_flags.cc:32] CUDA enabled.
    [i 1103 17:01:13.620734 16 cuda_flags.cc:32] CUDA enabled.
    F[i 1103 17:01:13.677055 16 cuda_flags.cc:32] CUDA enabled.
    [i 1103 17:01:13.688857 16 cuda_flags.cc:32] CUDA enabled.
    [i 1103 17:01:13.718698 16 cuda_flags.cc:32] CUDA enabled.
    [i 1103 17:01:13.723490 16 cuda_flags.cc:32] CUDA enabled.
    [i 1103 17:01:13.779703 16 cuda_flags.cc:32] CUDA enabled.
    [i 1103 17:01:13.783386 16 cuda_flags.cc:32] CUDA enabled.
    [i 1103 17:01:13.842737 16 cuda_flags.cc:32] CUDA enabled.
    [i 1103 17:01:13.845882 16 cuda_flags.cc:32] CUDA enabled.
    [i 1103 17:01:13.903293 16 cuda_flags.cc:32] CUDA enabled.
    [i 1103 17:01:13.909218 16 cuda_flags.cc:32] CUDA enabled.
    [i 1103 17:01:13.947171 16 cuda_flags.cc:32] CUDA enabled.
    [i 1103 17:01:13.953387 16 cuda_flags.cc:32] CUDA enabled.
    [i 1103 17:01:13.991226 16 cuda_flags.cc:32] CUDA enabled.
    [i 1103 17:01:14.005891 16 cuda_flags.cc:32] CUDA enabled.
    .
    ======================================================================
    FAIL: test_conv3d (__main__.TestCudnnConvOp)
    ----------------------------------------------------------------------
    Traceback (most recent call last):
      File "/home/liuzhian/anaconda3/envs/jittor/lib/python3.7/site-packages/jittor/test/test_cudnn_op.py", line 153, in test_conv3d
        check((2,4,10,10,10), (5,4,3,4,5), (1,1,1), (1,1,1))
      File "/home/liuzhian/anaconda3/envs/jittor/lib/python3.7/site-packages/jittor/test/test_cudnn_op.py", line 145, in check
        np.testing.assert_allclose(y.data, y2.data)
      File "/home/liuzhian/anaconda3/envs/jittor/lib/python3.7/site-packages/numpy/testing/_private/utils.py", line 1531, in assert_allclose
        verbose=verbose, header=header, equal_nan=equal_nan)
      File "/home/liuzhian/anaconda3/envs/jittor/lib/python3.7/site-packages/numpy/testing/_private/utils.py", line 844, in assert_array_compare
        raise AssertionError(msg)
    AssertionError: 
    Not equal to tolerance rtol=1e-07, atol=0
    
    Mismatched elements: 4623 / 7200 (64.2%)
    Max absolute difference: 5.340576e-05
    Max relative difference: 8.419145e-07
     x: array([[[[[26.199913, 32.35394 , 31.798569, ..., 27.34977 , 27.382778,
               20.727432],
              [34.28533 , 41.313995, 40.744705, ..., 36.90609 , 39.26516 ,...
     y: array([[[[[26.199905, 32.35394 , 31.79857 , ..., 27.349777, 27.38277 ,
               20.72743 ],
              [34.28532 , 41.314003, 40.744694, ..., 36.906097, 39.265167,...
    
    ----------------------------------------------------------------------
    Ran 5 tests in 1.481s
    
    FAILED (failures=1)
    
    

    I can pass the test process via python -m jittor.test.test_example

    My Envs

    • Ubuntu 16.04
    • g++ 5.4.0
    • cuda 10.0
    • cudnn 7.6.5
    • python 3.7 under conda
    opened by LiUzHiAn 2
  • Add init.trunc_normal_

    Add init.trunc_normal_

    Add init.trunc_normal_ For the PyTorch implementation, see https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/weight_init.py

    opened by liuruiyang98 0
  • Win11 相关Bug

    Win11 相关Bug

    Describe the bug

    系统:win11 环境:python3.9 框架:jittor:1.3.1.15

    1.根据官网执行命令,出现error C2065: 'M_PI': undeclared identifier报错

    python -m pip install jittor # 这一步成功执行
    python -m jittor.test.test_core # 这一步会报告 error C2065: 'M_PI': undeclared identifier
    

    当然运行如下测试代码依旧会报上述错误

    import jittor as jt
    a = jt.float32([1,2,3])
    print (a)
    print (a.data)
    
    1. 通过#274 修复该问题
    2. 之后继续执行上述安装命令:会出现RuntimeError: [f 1030 00:32:43.080000 64 py_obj_holder.h:36] numpy is not installed 通过numpy降级解决,请勿安装最新版本numpy==1.21.3,降级为1.20.3解决该问题,实测1.21.2也行。
    3. 继续执行上述安装命令:出现ImportError: cannot import name 'imaging' from 'PIL' (E:\anaconda3\envs\jittor\lib\site-packages\PIL_init.py),通过Pillow降级解决,请勿安装最新版本Pillow==8.4.0,降级为8.3.2解决。
    4. 安装成功,测试代码运行成功。
    5. 晚安,熬不住了。

    Full Log

    1 2 3

    opened by tupig-7 1
  • fix one bug in win11 jittor\src\misc\cpu_math.cc(47): error C2065: 'M…

    fix one bug in win11 jittor\src\misc\cpu_math.cc(47): error C2065: 'M…

    系统:Win11 环境:python3.8 框架:jittor:1.3.1.15 执行该命令:python -m jittor.test.test_core或者运行教程一的测试代码 出现以下问题: [e 1030 00:30:46.463000 84 log.cc:526] cpu_math.cc E:\anaconda3\envs\jittor\lib\site-packages\jittor\src\misc\cpu_math.cc(47): error C2065: 'M_PI': undeclared identifier E:\anaconda3\envs\jittor\lib\site-packages\jittor\src\misc\cpu_math.cc(53): note: see reference to function template instantiation 'float jittor::calc_erfinv(T)' being compiled with [ T=float ] E:\anaconda3\envs\jittor\lib\site-packages\jittor\src\misc\cpu_math.cc(48): error C2065: 'M_PI': undeclared identifier

    opened by tupig-7 0
  • wgan-gp训练导致segfault

    wgan-gp训练导致segfault

    Describe the bug

    wgan-gp训练导致segfault. Caught segfault at address 0x60, thread_name: 'C2', flush log... Segfault, exit [e 1013 21:41:41.304692 96 parallel_compiler.cc:318] Segfault happen, main thread exit

    训练代码来自wgan_gp

    Full Log

    Caught segfault at address 0x60, thread_name: 'C2', flush log... Segfault, exit [e 1013 21:41:41.304692 96 parallel_compiler.cc:318] Segfault happen, main thread exit *** Error in `python': corrupted double-linked list: 0x0000564dab9b8380 *** ======= Backtrace: ========= /lib/x86_64-linux-gnu/libc.so.6(+0x777f5)[0x7f03299997f5] /lib/x86_64-linux-gnu/libc.so.6(+0x80c81)[0x7f03299a2c81] /lib/x86_64-linux-gnu/libc.so.6(cfree+0x4c)[0x7f03299a658c] /usr/lib/x86_64-linux-gnu/libcuda.so.1(+0x1f4e17)[0x7f032452de17] /usr/lib/x86_64-linux-gnu/libcuda.so.1(+0x10a53a)[0x7f032444353a] /usr/lib/x86_64-linux-gnu/libcuda.so.1(cuModuleUnload+0x4f)[0x7f032459358f] /usr/local/cuda/lib64/libcudart.so(+0x2ebdf)[0x7f0328047bdf] /usr/local/cuda/lib64/libcudart.so(+0x302d3)[0x7f03280492d3] /usr/local/cuda/lib64/libcudart.so(+0x322c1)[0x7f032804b2c1] /usr/local/cuda/lib64/libcudart.so(+0x2543e)[0x7f032803e43e] /usr/local/cuda/lib64/libcudart.so(+0x1067a)[0x7f032802967a] /usr/local/cuda/lib64/libcudart.so(cudaDeviceSynchronize+0x59)[0x7f0328050bf9] /home/yinghui/.cache/jittor/jt1.3.1/g++5.4.0/py3.8.11/Linux-4.15.0-1x27/IntelRCoreTMi7x6f/default/cu10.0.130_sm_61/jittor_core.cpython-38-x86_64-linux-gnu.so(+0x22b823)[0x7f0325efc823] /lib/x86_64-linux-gnu/libc.so.6(+0x3a008)[0x7f032995c008] /lib/x86_64-linux-gnu/libc.so.6(+0x3a055)[0x7f032995c055] /home/yinghui/.cache/jittor/jt1.3.1/g++5.4.0/py3.8.11/Linux-4.15.0-1x27/IntelRCoreTMi7x6f/default/jit_utils_core.cpython-38-x86_64-linux-gnu.so(_ZN6jittor18segfault_sigactionEiP9siginfo_tPv+0x247)[0x7f03284240f7] /lib/x86_64-linux-gnu/libc.so.6(+0x354c0)[0x7f03299574c0] /home/yinghui/.cache/jittor/jt1.3.1/g++5.4.0/py3.8.11/Linux-4.15.0-1x27/IntelRCoreTMi7x6f/default/cu10.0.130_sm_61/jittor_core.cpython-38-x86_64-linux-gnu.so(_ZN6jittor18LoopVarAnalyzePass3runEv+0x30aa)[0x7f0325fa21ca] /home/yinghui/.cache/jittor/jt1.3.1/g++5.4.0/py3.8.11/Linux-4.15.0-1x27/IntelRCoreTMi7x6f/default/cu10.0.130_sm_61/jittor_core.cpython-38-x86_64-linux-gnu.so(_ZN6jittor11PassManager8run_passINS_18LoopVarAnalyzePassEEEvv+0x33f)[0x7f0325f42e8f] /home/yinghui/.cache/jittor/jt1.3.1/g++5.4.0/py3.8.11/Linux-4.15.0-1x27/IntelRCoreTMi7x6f/default/cu10.0.130_sm_61/jittor_core.cpython-38-x86_64-linux-gnu.so(_ZN6jittor11PassManager10run_passesEv+0xcb)[0x7f0325f3872b] /home/yinghui/.cache/jittor/jt1.3.1/g++5.4.0/py3.8.11/Linux-4.15.0-1x27/IntelRCoreTMi7x6f/default/cu10.0.130_sm_61/jittor_core.cpython-38-x86_64-linux-gnu.so(_ZN6jittor12TunerManager4tuneB5cxx11Ev+0x6c)[0x7f0325f60fec] /home/yinghui/.cache/jittor/jt1.3.1/g++5.4.0/py3.8.11/Linux-4.15.0-1x27/IntelRCoreTMi7x6f/default/cu10.0.130_sm_61/jittor_core.cpython-38-x86_64-linux-gnu.so(_ZN6jittor10OpCompiler10do_compileEPNS_2OpE+0xad)[0x7f0325eb7d3d] /home/yinghui/.cache/jittor/jt1.3.1/g++5.4.0/py3.8.11/Linux-4.15.0-1x27/IntelRCoreTMi7x6f/default/cu10.0.130_sm_61/jittor_core.cpython-38-x86_64-linux-gnu.so(+0x1c4dd9)[0x7f0325e95dd9] /home/yinghui/.cache/jittor/jt1.3.1/g++5.4.0/py3.8.11/Linux-4.15.0-1x27/IntelRCoreTMi7x6f/default/cu10.0.130_sm_61/jittor_core.cpython-38-x86_64-linux-gnu.so(_ZN6jittor12SimpleThread3runEv+0x147)[0x7f0325e9a367] /home/yinghui/miniconda3/envs/jittor/bin/../lib/libstdc++.so.6(+0xc92bd)[0x7f032835c2bd] /lib/x86_64-linux-gnu/libpthread.so.0(+0x76ba)[0x7f0329cf36ba] /lib/x86_64-linux-gnu/libc.so.6(clone+0x6d)[0x7f0329a2951d] ======= Memory map: ======== 200000000-200200000 rw-s 00000000 00:06 443 /dev/nvidiactl 200200000-200400000 ---p 00000000 00:00 0 200400000-200600000 rw-s 00000000 00:06 443 /dev/nvidiactl 200600000-202600000 rw-s 00000000 00:06 443 /dev/nvidiactl 202600000-205600000 rw-s 00000000 00:06 443 /dev/nvidiactl 205600000-206200000 ---p 00000000 00:00 0 206200000-206400000 rw-s 00000000 00:06 443 /dev/nvidiactl 206400000-206600000 rw-s 00000000 00:06 443 /dev/nvidiactl 206600000-206800000 rw-s 206600000 00:06 490 /dev/nvidia-uvm 206800000-206a00000 ---p 00000000 00:00 0 206a00000-206c00000 rw-s 00000000 00:06 443 /dev/nvidiactl 206c00000-500200000 ---p 00000000 00:00 0 10000000000-10404000000 ---p 00000000 00:00 0 564d7bd9e000-564d7bdfd000 r--p 00000000 fd:02 34000027 /home/yinghui/miniconda3/envs/jittor/bin/python3.8 564d7bdfd000-564d7bff4000 r-xp 0005f000 fd:02 34000027 /home/yinghui/miniconda3/envs/jittor/bin/python3.8 564d7bff4000-564d7c0db000 r--p 00256000 fd:02 34000027 /home/yinghui/miniconda3/envs/jittor/bin/python3.8 564d7c0dc000-564d7c0e1000 r--p 0033d000 fd:02 34000027 /home/yinghui/miniconda3/envs/jittor/bin/python3.8 564d7c0e1000-564d7c119000 rw-p 00342000 fd:02 34000027 /home/yinghui/miniconda3/envs/jittor/bin/python3.8 564d7c119000-564d7c139000 rw-p 00000000 00:00 0 564d7ca0d000-564daae38000 rw-p 00000000 00:00 0 [heap] 564daae38000-564daae39000 ---p 00000000 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/usr/local/cuda-10.0/lib64/libcublas.so.10.0.130 7f02b3929000-7f02b396d000 rw-p 04340000 fd:02 92545858 /usr/local/cuda-10.0/lib64/libcublas.so.10.0.130 7f02b396d000-7f02b397f000 rw-p 00000000 00:00 0 7f02b397f000-7f02b5599000 r-xp 00000000 fd:02 92019958 /usr/lib/x86_64-linux-gnu/libcublasLt.so.10.2.2.89 7f02b5599000-7f02b5798000 ---p 01c1a000 fd:02 92019958 /usr/lib/x86_64-linux-gnu/libcublasLt.so.10.2.2.89 7f02b5798000-7f02b5809000 rw-p 01c19000 fd:02 92019958 /usr/lib/x86_64-linux-gnu/libcublasLt.so.10.2.2.89Aborted (core dumped)

    Minimal Reproduce

    训练代码来自wgan_gp 应该是计算gradient penalty导致的问题。

    opened by yinghdb 1
  • Mob2

    Mob2

    mobile ver merge,

    opened by Exusial 0
  • fp16特性

    fp16特性

    请问jittor目前可以开启混合精度(fp16)吗?我看部分代码里是有的,但是不知道怎么开启

    opened by sunhm15 1
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Website | Documentation | Tutorials | Installation | Release Notes CatBoost is a machine learning method based on gradient boosting over decision tree

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A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

Website | Documentation | Tutorials | Installation | Release Notes CatBoost is a machine learning method based on gradient boosting over decision tree

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Intel® Nervana™ reference deep learning framework committed to best performance on all hardware

DISCONTINUATION OF PROJECT. This project will no longer be maintained by Intel. Intel will not provide or guarantee development of or support for this

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Intel® Nervana™ reference deep learning framework committed to best performance on all hardware

DISCONTINUATION OF PROJECT. This project will no longer be maintained by Intel. Intel will not provide or guarantee development of or support for this

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ML-Ensemble – high performance ensemble learning

A Python library for high performance ensemble learning ML-Ensemble combines a Scikit-learn high-level API with a low-level computational graph framew

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